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| 1 | Hepatitis B virus therapy:What's the future holding for us?显示文摘Hepatitis B is one of the leading causes of liver cancer worldwide and unfortunately the number of people affected with hepatitis B virus(HBV) infection is still on the rise. Although the HBV has been known to cause fatal illness since decades but the population effected by this lethal virus have still only a few options for its management. The major treatment strategies include interferons and nucleos(t)ide analogues. These agents have so far produced unsatisfactory results in terms of complete virus eradication. Interferons cannot be used for long term therapy because of their potential side effects. Prolong treatment with nucleos(t)ide analogues has also been reported to cause serious side effects besides the increasing resistance by the virus. The need for new innovative solutions for treatment of HBV has been realized by global research institutes and pharmaceutical industry. Present review focuses in detail on the new ideas that are being transformed into therapeutic tools for use as future therapies in HBV infection. Modern drug designing and screening methods have made the drug discovery process shorter and more reliable. HBV therapeutics will take a new turn in coming years owing to these intelligent drug designing and screening methods. Future therapy of HBV is aiming to include the use of vaccines(both prophylactic and therapeutic), immunomodulators such as antibodies, non-nucleoside antivirals such as RNAi and inhibitors of viral life cycle. | Sobia Manzoor Muhammad Saalim Muhammad Imran Saleha Resham Javed Ashraf | 2015 | World Journal of Gastroenterology2015，21，44： | 9 |
| 2 | Phyto-Toxicity of Chromium in Maize: Oxidative Damage, Osmolyte Accumulation, Anti-Oxidative Defense and Chromium Uptake显示文摘Agricultural production systems are immensely exposed to different environmental stresses in which heavy metal stress receives serious concerns. This study was conducted to explore the deleterious effects of different chromium(Cr) stress levels, i.e., 0, 30, 6090, 120, and 150 μmol L-1, on two maize genotypes, Wandan 13 and Runnong 35. Both genotypes were evaluated by measuring their growth and yield characteristics, Cr accumulation in different plant tissues, alterations in osmolyte accumulation, generation o reactive oxygen species(ROS), and anti-oxidative enzyme activity to scavenge ROS. The results showed that Cr stress decreased the leaf area, cob formation, 100-grain weight, shoot fresh biomass, and yield formation, while Cr accumulation in different maize tissues was found in the order of roots > leaves > stem > seeds in both genotypes. The increased Cr toxicity resulted in higher free proline soluble sugars and total phenolic contents, and lower soluble protein contents. However, enhanced lipid peroxidation was noticed in the forms of malondialdehyde, hydrogen peroxide(H2O2) and thiobarbituric acid reactive substance accumulation, and electrolyte leakage. The hyperactivity of superoxide dismutase, peroxidase, catalase, ascorbate peroxidase, especially glutathione peroxidase and glutathione reductase indicated that these anti-oxidative enzymes had a central role in protecting maize from Cr toxicity, especially for Wandan 13. Moreover, higher uptake and less translocation of Cr contents into the grains of Wandan 13 implied its importance as a potential candidate against soil Cr pollution. | Shakeel Ahmad ANJUM Umair ASHRAF Imran KHAN Mohsin TANVEER Muhammad SHAHID Abdul SHAKOOR WANG Longchang | 2017 | Pedosphere2017，27，2： | 3 |
| 3 | Mutagenic and cytotoxic potential of Endosulfan and Lambda-cyhalothrin—In vitro study describing individual and combined effects of pesticides显示文摘Excessive use of pesticides poses increased risks to non target species including humans. In the developing countries, lack of proper awareness about the toxic potential of pesticides makes the farmer more vulnerable to pesticide linked toxicities, which could lead to diverse pathological conditions. The toxic potential of a pesticide could be determined by their ability to induce genetic mutations and cytotoxicity. Hence, determination of genetic mutation and cytotoxicity of each pesticide is unavoidable to legislate health and safety appraisal about pesticides. The objective of current investigation was to determine the genotoxic and cytotoxic potential of Endosulfan(EN) and Lambda-cyhalothrin(LC); individually and in combination. 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide(MTT) assay was utilized to determine cytotoxicity, while two mutant histidine dependent Salmonella strains(TA98, TA100) were used to determine the mutagenicity of EN and LC.Moreover, mutagenicity assay was conducted with and without S9 to evaluate the effects of metabolic activation on mutagenicity. Even though a dose dependent increase in the number of revertant colonies was detected with EN against both bacterial strains, a highly significant(p < 0.05) increase in the mutagenicity was detected in TA98 with S9. In comparison, data obtained from LC revealed less mutagenic potential than EN. Surprisingly,the non-mutagenic individual-concentrations of EN and LC showed dose dependent mutagenicity when combined. Combination of EN and LC synergistically induced mutagenicity both in TA98 and TA100. MTT assay spotlighted comparable dose dependent cytotoxicity effects of both pesticides. Interestingly, the combination of EN and LC produced increased reversion and cytotoxicity at lower doses as compared to each pesticide, concluding that pesticide exposure even at sub-lethal doses can produce cytotoxicity and genetic mutations, which could lead to carcinogenicity. | Umber Saleem Sohail Ejaz Muhammad Ashraf Muhammad Ovais Omer Imran Iltaf Zainab Batool Riffat Fatima Msbah Afzal | 2014 | Journal of Environmental Sciences2014，26，7： | 3 |
| 4 | Perspectives of Using L-Tryptophan for Improving Productivity of Agricultural Crops: A Review显示文摘Plant growth regulators are biologically active signaling molecules that regulate a number of plant physiological processes. Auxin(indole-3-acetic acid) is an important plant growth regulator and is synthesized within plant tissues through L-tryptophan(L-TRP)-dependent and-independent pathways. It has been found that plants respond to exogenously applied L-TRP due to insufficient endogenous auxin biosynthesis. The exogenous application of L-TRP is highly significant for normal plant growth and development.L-tryptophan is applied through foliar spray, seed priming, and soil application. Soil-applied L-TRP is either directly taken up by plants or metabolized to auxin by soil microbiota and then absorbed by plant roots. Similarly, foliar spray and seed priming with L-TRP stimulates auxin synthesis within plants and improves the growth and productivity of agricultural crops. Furthermore, L-TRP contains approximately 14% nitrogen(N) in its composition, which is released upon its metabolism within a plant or in the rhizosphere and plays a role in enhancing crop productivity. This review deals with assessing crop responses under the exogenous application of L-TRP in normal and stressed environments, mode of action of L-TRP, advantages of using L-TRP over other auxin precursors, and role of the simultaneous use of L-TRP and auxin-producing microbes in improving the productivity of agricultural crops. To the best of our knowledge, this is the first review reporting the importance of the use of L-TRP in agriculture. | Ayesha MUSTAFA Muhammad IMRAN Muhammad ASHRAF Khalid MAHMOOD | 2018 | Pedosphere2018，28，1： | 2 |
| 5 | The pre–mild cognitive impairment, subjective cognitive impairment stage of Alzheimer’s disease显示文摘 | Barry Reisberg Leslie Prichep Lisa Mosconi E. Roy John Lidia Glodzik-Sobanska Istvan Boksay Isabel Monteiro Carol Torossian Alok Vedvyas Nauman Ashraf Imran A. Jamil Mony J. de Leon | 2008 | Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association2008，，1： | 1 |
| 6 | Role of viral and host factors in interferon based therapy of hepatitis C virus infection 显示文摘 | Imran M1 Manzoor S Ashraf J | 2013 | VirolJ2013，10，： | 1 |
| 7 | Privacy Protection with Dynamic Pseudonym-Based Multiple Mix-Zones Over Road Networks显示文摘In this research we proposed a strategy for location privacy protection which addresses the issues related with existing location privacy protection techniques. Mix-Zones and pseudonyms are considered as the basic building blocks for location privacy; however, continuously changing pseudonyms process at multiple locations can enhance user privacy. It has been revealed that changing pseudonym at improper time and location may threat to user's privacy. Moreover, certain methods related to pseudonym change have been proposed to attain desirable location privacy and most of these solutions are based upon velocity, GPS position and direction of angle. We analyzed existing methods related to location privacy with mix zones, such as RPCLP, EPCS and MODP, where it has been observed that these methods are not adequate to attain desired level of location privacy and suffered from large number of pseudonym changes. By analyzing limitations of existing methods, we proposed Dynamic Pseudonym based multiple mix zone(DPMM) technique, which ensures highest level of accuracy and privacy. We simulate our data by using SUMO application and analysis results has revealed that DPMM outperformed existing pseudonym change techniques and achieved better results in terms of acquiring high privacy with small number of pseudonym change. | Qasim Ali Arain Zhongliang Deng Imran memon Asma Zubedi Jichao Jiao Aisha Ashraf Muhammad Saad Khan | 2017 | China Communications2017，14，4： | 1 |
| 8 | Ursodeoxycholic acid in patients with ulcerative colitis and primary sclerosing cholangitis for prevention of colon cancer: a meta-analysis显示文摘 | Imran Ashraf Abhishek Choudhary Murtaza Arif Michelle Matteson Hazem Hammad Srinivas Puli Matthew Bechtold | 2012 | Indian Journal of Gastroenterology2012，，2： | 1 |
| 9 | A Multilevel Deep Feature Selection Framework for Diabetic Retinopathy Image Classification显示文摘Diabetes or Diabetes Mellitus(DM)is the upset that happens due to high glucose level within the body.With the passage of time,this polygenic disease creates eye deficiency referred to as Diabetic Retinopathy(DR)which can cause a major loss of vision.The symptoms typically originate within the retinal space square in the form of enlarged veins,liquid dribble,exudates,haemorrhages and small scale aneurysms.In current therapeutic science,pictures are the key device for an exact finding of patients’illness.Meanwhile,an assessment of new medicinal symbolisms stays complex.Recently,Computer Vision(CV)with deep neural networks can train models with high accuracy.The thought behind this paper is to propose a computerized learning model to distinguish the key precursors of Dimensionality Reduction(DR).The proposed deep learning framework utilizes the strength of selected models(VGG and Inception V3)by fusing the extracated features.To select the most discriminant features from a pool of features,an entropy concept is employed before the classification step.The deep learning models are fit for measuring the highlights as veins,liquid dribble,exudates,haemorrhages and miniaturized scale aneurysms into various classes.The model will ascertain the loads,which give the seriousness level of the patient’s eye.The model will be useful to distinguish the correct class of seriousness of diabetic retinopathy pictures. | Farrukh Zia Isma Irum Nadia Nawaz Qadri Yunyoung Nam Kiran Khurshid Muhammad Ali Imran Ashraf Muhammad Attique Khan | 2022 | Computers, Materials & Continua2022，，2： | 1 |
| 10 | Embedded Coded Relay System for Molecular Communications显示文摘With the emergence of the COVID-19 pandemic,the World Health Organization(WHO)has urged scientists and industrialists to exploremodern information and communication technology(ICT)as a means to reduce or even eliminate it.The World Health Organization recently reported that the virus may infect the organism through any organ in the living body,such as the respiratory,the immunity,the nervous,the digestive,or the cardiovascular system.Targeting the abovementioned goal,we envision an implanted nanosystem embedded in the intra living-body network.The main function of the nanosystem is either to perform diagnosis and mitigation of infectious diseases or to implement a targeted drug delivery system(i.e.,delivery of the therapeutic drug to the diseased tissue or targeted cell).The communication among the nanomachines is accomplished via communication-based molecular diffusion.The control/interconnection of the nanosystem is accomplished through the utilization of Internet of bio-nano things(IoBNT).The proposed nanosystem is designed to employ a coded relay nanomachine disciplined by the decode and forward(DF)principle to ensure reliable drug delivery to the targeted cell.Notably,both the sensitivity of the drug dose and the phenomenon of drug molecules loss before delivery to the target cell site in long-distance due to the molecules diffusion process are taken into account.In this paper,a coded relay NM with conventional coding techniques such as RS and Turbo codes is selected to achieve minimum bit error rate(BER)performance and high signal-to-noise ratio(SNR),while the detection process is based on maximum likelihood(ML)probability and minimum error probability(MEP).The performance analysis of the proposed scheme is evaluated in terms of channel capacity and bit error rate by varying system parameters such as relay position,number of released molecules,relay and receiver size.Analysis results are validated through simulation and demonstrate that the proposed scheme can significantly improve delivery performance of the desirable drugs in the molecular communication system. | Eman S.Attia Ashraf A.M.Khalaf Fathi E.Abd El-Samie Saied M.Abd El-atty Konstantinos A.Lizos Osama Alfarraj Farid Shawki Imran Khan Ki-Il Kim | 2022 | Computers, Materials & Continua2022，，8： | 1 |
| 11 | Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s显示文摘In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting firearms.which is why an automated weapon detection system is needed.Various automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good results.However,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system.These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos.This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter.The proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm.The proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 score.The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved.Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN.It is promising to be used in the field of security and weapon detection. | Abdul Hanan Ashraf Muhammad Imran Abdulrahman M.Qahtani Abdulmajeed Alsufyani Omar Almutiry Awais Mahmood Muhammad Attique Mohamed Habib | 2022 | Computers, Materials & Continua2022，，2： | 1 |
| 12 | Outcome of open transcorticalapproach in the management of intraventricular lesions显示文摘 | Mazher S Imran M Ashraf | 2013 | J CollPhysicians Surg Pak2013，23，12： | 1 |
| 13 | Synthesis, characterization and in vitro hydrolysis studies of ester and antide prodrugs of dexibuprolen 显示文摘 | ASHRAF Z IMRAN M AM1N S | 2012 | Med Chem Res2012，21，11： | 1 |
| 14 | Central Aggregator Intrusion Detection System for Denial of Service Attacks显示文摘Vehicle-to-grid technology is an emerging field that allows unused power from Electric Vehicles(EVs)to be used by the smart grid through the central aggregator.Since the central aggregator is connected to the smart grid through a wireless network,it is prone to cyber-attacks that can be detected and mitigated using an intrusion detection system.However,existing intrusion detection systems cannot be used in the vehicle-to-grid network because of the special requirements and characteristics of the vehicle-to-grid network.In this paper,the effect of denial-of-service attacks of malicious electric vehicles on the central aggregator of the vehicle-to-grid network is investigated and an intrusion detection system for the vehicle-to-grid network is proposed.The proposed system,central aggregator–intrusion detection system(CA-IDS),works as a security gateway for EVs to analyze andmonitor incoming traffic for possible DoS attacks.EVs are registered with a Central Aggregator(CAG)to exchange authenticated messages,and malicious EVs are added to a blacklist for violating a set of predefined policies to limit their interaction with the CAG.A denial of service(DoS)attack is simulated at CAG in a vehicle-to-grid(V2G)network manipulating various network parameters such as transmission overhead,receiving capacity of destination,average packet size,and channel availability.The proposed system is compared with existing intrusion detection systems using different parameters such as throughput,jitter,and accuracy.The analysis shows that the proposed system has a higher throughput,lower jitter,and higher accuracy as compared to the existing schemes. | Sajjad Ahmad Imran Raza MHasan Jamal Sirojiddin Djuraev Soojung Hur Imran Ashraf | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 15 | Wi-Fi Positioning Dataset with Multiusers and Multidevices Considering Spatio-Temporal Variations显示文摘Precise information on indoor positioning provides a foundation for position-related customer services.Despite the emergence of several indoor positioning technologies such as ultrawideband,infrared,radio frequency identification,Bluetooth beacons,pedestrian dead reckoning,and magnetic field,Wi-Fi is one of the most widely used technologies.Predominantly,Wi-Fi fingerprinting is the most popular method and has been researched over the past two decades.Wi-Fi positioning faces three core problems:device heterogeneity,robustness to signal changes caused by human mobility,and device attitude,i.e.,varying orientations.The existing methods do not cover these aspects owing to the unavailability of publicly available datasets.This study introduces a dataset that includes the Wi-Fi received signal strength(RSS)gathered using four different devices,namely Samsung Galaxy S8,S9,A8,LG G6,and LG G7,operated by three surveyors,including a female and two males.In addition,three orientations of the smartphones are used for the data collection and include multiple buildings with a multifloor environment.Various levels of human mobility have been considered in dynamic environments.To analyze the time-related impact on Wi-Fi RSS,data over 3 years have been considered. | Imran Ashraf Sadia Din Soojung Hur Yongwan Park | 2022 | Computers, Materials & Continua2022，，3： | 0 |
| 16 | Smart Techniques for LULC Micro Class Classification Using Landsat8 Imagery显示文摘Wheat species play important role in the price of products and wheat production estimation.There are several mathematical models used for the estimation of the wheat crop but these models are implemented without considering the wheat species which is an important independent variable.The task of wheat species identification is challenging both for human experts as well as for computer vision-based solutions.With the use of satellite remote sensing,it is possible to identify and monitor wheat species on a large scale at any stage of the crop life cycle.In this work,nine popular wheat species are identified by using Landsat8 operational land imager(OLI)and thermal infrared sensor(TIRS)data.Two thousand samples of eachwheat crop species are acquired every fifteen days with a temporal resolution of ten multispectral bands(band two to band eleven).This study employs random forest(RF),artificial neural network,support vector machine,Naive Bayes,and logistic regression for nine types of wheat classification.In addition,deep neural networks are also developed.Experimental results indicate that RF shows the best performance of 91%accuracy while DNN obtains a 90.2%accuracy.Results suggest that remotely sensed data can be used in wheat type estimation and to improve the performance of the mathematical models. | Mutiullah Jamil Hafeez ul Rehman SaleemUllah Imran Ashraf Saqib Ubaid | 2023 | Computers, Materials & Continua2023，，3： | 0 |
| 17 | Gastrointestinal Diseases Classification Using Deep Transfer Learning and Features Optimization显示文摘Gastrointestinal diseases like ulcers, polyps’, and bleeding areincreasing rapidly in the world over the last decade. On average 0.7 millioncases are reported worldwide every year. The main cause of gastrointestinaldiseases is a Helicobacter Pylori (H. Pylori) bacterium that presents in morethan 50% of people around the globe. Many researchers have proposeddifferent methods for gastrointestinal disease using computer vision techniques.Few of them focused on the detection process and the rest of themperformed classification. The major challenges that they faced are the similarityof infected and healthy regions that misleads the correct classificationaccuracy. In this work, we proposed a technique based on Mask Recurrent-Convolutional Neural Network (R-CNN) and fine-tuned pre-trainedResNet-50 and ResNet-152 networks for feature extraction. Initially, the region ofinterest is detected using Mask R-CNN which is later utilized for the trainingof fine-tuned models through transfer learning. Features are extracted fromfine-tuned models that are later fused using a serial approach. Moreover, anImproved Ant Colony Optimization (ACO) algorithm has also opted for thebest feature selection from the fused feature vector. The best-selected featuresare finally classified using machine learning techniques. The experimentalprocess was conducted on the publicly available dataset and obtained animproved accuracy of 96.43%. In comparison with state-of-the-art techniques,it is observed that the proposed accuracy is improved. | Mousa Alhajlah Muhammad Nouman Noor Muhammad Nazir Awais Mahmood Imran Ashraf Tehmina Karamat | 2023 | Computers, Materials & Continua2023，，4： | 0 |
| 18 | Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model显示文摘Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate diagnosis.Machine learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD early.This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning repository.The research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for CKD.Furthermore,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance problems.To ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning models.The proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for detectingCKD.This in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD. | Nazik Alturki Abdulaziz Altamimi Muhammad Umer Oumaima Saidani Amal Alshardan Shtwai Alsubai Marwan Omar Imran Ashraf | 2024 | Computer Modeling in Engineering & Sciences2024，139，6： | 0 |
| 19 | MagneFi: Multiuser, Multi-Building and Multi-Floor Geomagnetic Field Dataset for Indoor Positioning显示文摘Indoor positioning and localization have emerged as a potential research area during the last few years owing to the wide proliferation of smartphones and the inception of location-attached services for the consumer industry.Due to the importance of precise location information,several positioning technologies are adopted such as Wi-Fi,ultrawideband,infrared,radio frequency identification,Bluetooth beacons,pedestrian dead reckoning,and magnetic field,etc.Although Wi-Fi and magnetic field-based positioning are more attractive concerning the deployment of Wi-Fi access points and ubiquity of magnetic field data,the latter is preferred as it does not require any additional infrastructure as other approaches do.Despite the advantages of magnetic field positioning,comparing the performance of positioning and localization algorithms is very difficult due to the lack of good public datasets that cover various aspects of the magnetic field data.Available datasets do not provide the data to analyze the impact of device heterogeneity,user heights,and time-specific magnetic field mutation.Moreover,multi-floor and multibuilding data are available for the evaluation of state-of-the-art approaches.To overcome the above-mentioned issues,this study presents multi-user,multidevice,multi-building magnetic field data which is collected over a longer period.The dataset contains the data from five different smartphones including Samsung Galaxy S8,S9,A8,LG G6,and LG G7 for three geographically separated buildings.Three users including one female and two males collected the data for various path geometry and data collection scenarios.Moreover,the data contains the magnetic field samples collected on stairs to test multifloor localization.Besides the magnetic field data,the data from inertial measurement unit sensors like the accelerometer,motion sensors,and barometer is provided as well. | Imran Ashraf Muhammad Usman Ali Soojung Hur Gunzung Kim Yongwan Park | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 20 | Malicious Traffic Detection in IoT and Local Networks Using Stacked Ensemble Classifier显示文摘Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity.Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic.Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage.Currently,many automated systems can detect malicious activity,however,the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems.The present study focuses on the detection of malicious traffic with high accuracy using machine learning techniques.The proposed approach used two datasets UNSW-NB15 and IoTID20 which contain the data for IoT-based traffic and local network traffic,respectively.Both datasets were combined to increase the capability of the proposed approach in detecting malicious traffic from local and IoT networks,with high accuracy.Horizontally merging both datasets requires an equal number of features which was achieved by reducing feature count to 30 for each dataset by leveraging principal component analysis(PCA).The proposed model incorporates stacked ensemble model extra boosting forest(EBF)which is a combination of tree-based models such as extra tree classifier,gradient boosting classifier,and random forest using a stacked ensemble approach.Empirical results show that EBF performed significantly better and achieved the highest accuracy score of 0.985 and 0.984 on the multi-domain dataset for two and four classes,respectively. | R.D.Pubudu L.Indrasiri Ernesto Lee Vaibhav Rupapara Furqan Rustam Imran Ashraf | 2022 | Computers, Materials & Continua2022，，4： | 0 |
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