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| 1 | Variations of precipitation characteristics during the period 1960–2014 in the Source Region of the Yellow River,China显示文摘Precipitation, a natural feature of weather systems in the Earth, is vitally important for the environment of any region. Under global climate change condition, the characteristics of precipitation have changed as a consequence of enhanced global hydrological cycle. The source region of the Yellow River(SRYR), locating within the Qinghai-Tibet Plateau, is sensitive to the global climate change due to its complex orography and fragile ecosystem. To understand the precipitation characteristics and its impacts on the environment in the region, we studied the characteristics of rainy days and precipitation amount of different precipitation classes, such as light(0–5 and 5–10 mm), moderate(10–15, 15–20 and 20–25 mm) and heavy(≥25 mm) rains by analyzing the precipitation data of typical meteorological stations in the SRYR during the period 1961–2014, as well as the trends of persistent rainfall events and drought events. Results showed that annual average precipitation in this area had a non-significant(P>0.05) increasing trend, and 82.5% of the precipitation occurred from May to September. Rainy days of the 0–5 mm precipitation class significantly decreased, whereas the rainy days of 5–10, 10–15, and 20–25 mm precipitation classes increased and that of ≥25 mm precipitation class decreased insignificantly. The persistent rainfall events of 1-or 2-day and more than 2-day showed an increasing trend, with the 1-or 2-day events being more frequent. Meanwhile, the number of short drought periods(≤10 days) increased while long drought periods(>10 days) decreased. Since the 0–5 mm precipitation class had a huge impact on the grasslands productivity; the 5–10, 10–15, and 20–25 mm precipitation classes had positive effects on vegetation which rely on the deep soil water through moving nutrients and water into the root zone of these vegetation or through the plant-microbe interactions; the ≥25 mm precipitation class contributed to the floods; and more persistent rainfall events and fewer long drought events inferred positive effects on agriculture. Thus, these results indicate grassland degradation, less risk of floods, and the upgrading impact of climate change on agriculture. This study may provide scientific knowledge for policymakers to sustain the eco-environmental resources in the SYSR. | Mudassar IQBAL WEN Jun WANG Shaoping TIAN Hui Muhammad ADNAN | 2018 | Journal of Arid Land2018，10，3： | 6 |
| 2 | Factors influencing hybrid maize farmers' risk attitudes and their perceptions in Punjab Province,Pakistan显示文摘Hybrid maize farmers have to face diverse kinds of climate, biological, price and financial risks. Farmers' risk perceptions and risk attitudes are essential elements influencing farm operations and management decisions. However, this important issue has been overlooked in the contemporary studies and therefore there is a dearth of literature on this important issue. The present research is therefore, an attempt to fill this gap. This study aims to quantify hybrid maize farmers' perceptions of disastrous risks, their attitudes towards risk and to explore the impacts of various farm and farm household factors on farmers' risk attitudes and risk perceptions. The present study is conducted in four hybrid maize growing districts of Punjab Province, Pakistan, using cross-sectional data of 400 hybrid maize farmers. Risk matrix and equally likely certainty equivalent(ELCE) method are used to rank farmers' perceptions of four catastrophic risk sources including climate, biological, price and financial risks and to investigate farmers' risk aversion attitudes, respectively. Furthermore, probit regression is used to analyze the determinants affecting farmers' risk attitudes and risk perceptions. The results of the study showed that majority of farmers are risk averse in nature and perceive price, biological and climate to be potential sources of risks to their farm enterprise. In addition, analysis divulges that distance from farm to main market, off-farm income, location dummies for Sahiwal and Okara, age, maize farming experience, access to extension agent, significantly(either negatively or positively) influence farmers' risk attitudes and risk perceptions. The study delivers valuable insights for farmers, agricultural insurance sector, extension services researchers and agricultural policy makers about the local understanding of risks to hybrid maize crop in developing countries, like Pakistan, and have implications for research on farmers' adaptation to exposed risks. | Shoaib Akhtar LI Gu-cheng Raza Ullah Adnan Nazir Muhammad Amjed Iqbal Muhammad Haseeb Raza Nadeem Iqbal Muhammad Faisal | 2018 | Journal of Integrative Agriculture2018，17，6： | 3 |
| 3 | Implementation of Efficient B2G and V2G in Practical Cases显示文摘In this paper, building to grid(B2G) and vehicle to grid(V2G) have been defined with clear and practical understanding. Both of them are new generation technologies which are the essential part of smart city living and crowd energy clustering. Firstly, an in-detailed overview has been provided with an introduction to B2G and V2G followed by a historical overview and theoretical analysis in respect to smart city planning. Next, a review is conducted on current and previous smart living research, which deals with B2G and V2G. Efficient B2G and V2G implementations in practical cases then have been discussed. Lastly, both of these technical prospects have been analyzed in crowd energy diagram. | MD Shahrukh Adnan Khan Kazi Mahtab Kadir Md.Ibrahim Ibne Alam Md.Khairul Alam Jianhui Wong Aseef Iqbal | 2018 | Journal of Electronic Science and Technology2018，16，4： | 2 |
| 4 | An Efficient Internet Traffic Classification System Using Deep Learning for IoT显示文摘Internet of Things(IoT)defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location.These IoT devices are connected to a network therefore prone to attacks.Various management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic classification.Due to the ineffectiveness of traditional classification schemes,such as port-based and payload-based methods,researchers proposed machine learning-based traffic classification systems based on shallow neural networks.Furthermore,machine learning-based models incline to misclassify internet traffic due to improper feature selection.In this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic.To examine the performance of the proposed technique,Moore-dataset is used for training the classifier.The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network(DNN).In particular,the maximum entropy classifier is used to classify the internet traffic.The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification,i.e.,99.23%.Furthermore,the proposed algorithm achieved the highest accuracy compared to the support vector machine(SVM)based classification technique and k-nearest neighbours(KNNs)based classification technique. | Muhammad Basit Umair Zeshan Iqbal Muhammad Bilal Jamel Nebhen Tarik Adnan Almohamad Raja Majid Mehmood | 2022 | Computers, Materials & Continua2022，，4： | 1 |
| 5 | Projection of future streamflow of the Hunza River Basin,Karakoram Range(Pakistan)using HBV hydrological model显示文摘Hydrologiska Byrans Vattenbalansavdeling(HBV) Light model was used to evaluate the performance of the model in response to climate change in the snowy and glaciated catchment area of Hunza River Basin. The study aimed to understand the temporal variation of streamflow of Hunza River and its contribution to Indus River System(IRS). HBV model performed fairly well both during calibration(R2=0.87, Reff=0.85, PBIAS=-0.36) and validation(R2=0.86, Reff=0.83, PBIAS=-13.58) periods on daily time scale in the Hunza River Basin. Model performed better on monthly time scale with slightly underestimated low flows period during bothcalibration(R2=0.94, Reff=0.88, PBIAS=0.47) and validation(R2=0.92, Reff=0.85, PBIAS=15.83) periods. Simulated streamflow analysis from 1995-2010 unveiled that the average percentage contribution of snow, rain and glacier melt to the streamflow of Hunza River is about 16.5%, 19.4% and 64% respectively. In addition, the HBV-Light model performance was also evaluated for prediction of future streamflow in the Hunza River using future projected data of three General Circulation Model(GCMs) i.e. BCC-CSM1.1, CanESM2, and MIROCESM under RCP2.6, 4.5 and 8.5 and predictions were made over three time periods, 2010-2039, 2040-2069 and 2070-2099, using 1980-2010 as the control period. Overall projected climate results reveal that temperature and precipitation are the most sensitiveparameters to the streamflow of Hunza River. MIROC-ESM predicted the highest increase in the future streamflow of the Hunza River due to increase in temperature and precipitation under RCP4.5 and 8.5 scenarios from 2010-2099 while predicted slight increase in the streamflow under RCP2.6 during the start and end of the 21 th century. However, BCCCSM1.1 predicted decrease in the streamflow under RCP8.5 due to decrease in temperature and precipitation from 2010-2099. However, Can ESM2 predicted 22%-88% increase in the streamflow under RCP4.5 from 2010-2099. The results of this study could be useful for decision making and effective future strategic plans for water management and their sustainability in the region. | Ayaz Fateh ALI XIAO Cun-de ZHANG Xiao-peng Muhammad ADNAN Mudassar IQBAL Garee KHAN | 2018 | Journal of Mountain Science2018，15，10： | 1 |
| 6 | Autonomous Parking-Lots Detection with Multi-Sensor Data Fusion Using Machine Deep Learning Techniques显示文摘The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity.Vision-based target detection and object classification have been improved due to the development of deep learning algorithms.Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise,well-engineered,and complete detection of objects,scene or events.The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection.In this study we examined to solve these problems described by(1)extracting region-of-interest in the images(2)vehicle detection based on instance segmentation,and(3)building deep learning model based on the key features obtained from input parking images.We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces.Image augmentation techniques were performed using edge detection,cropping,refined by rotating,thresholding,resizing,or color augment to predict the region of bounding boxes.A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling,training,validating and testing on parking video frames through video-camera.The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6%than previous reported methodologies.Moreover,this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection.The results are verified using Python,TensorFlow,OpenCV computer simulation frameworks. | Kashif Iqbal Sagheer Abbas Muhammad Adnan Khan Atifa Ather Muhammad Saleem Khan Areej Fatima Gulzar Ahmad | 2021 | Computers, Materials & Continua2021，，2： | 1 |
| 7 | An energy-efficient link layer pro- tocol for reliable transmission over wireless networks 显示文摘 | Adnan Iqbal Syed Ali Khayam | 2009 | EUR- ASIP Journal on Wireless Communications and Networking2009，26，28： | 1 |
| 8 | Automated Patient Discomfort Detection Using Deep Learning显示文摘The Internet of Things(IoT)has been transformed almost all fields of life,but its impact on the healthcare sector has been notable.Various IoTbased sensors are used in the healthcare sector and offer quality and safe care to patients.This work presents a deep learning-based automated patient discomfort detection system in which patients’discomfort is non-invasively detected.To do this,the overhead view patients’data set has been recorded.For testing and evaluation purposes,we investigate the power of deep learning by choosing a Convolution Neural Network(CNN)based model.The model uses confidence maps and detects 18 different key points at various locations of the body of the patient.Applying association rules and part affinity fields,the detected key points are later converted into six main body organs.Furthermore,the distance of subsequent key points is measured using coordinates information.Finally,distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions.The accuracy of the proposed system is assessed on various test sequences.The experimental outcomes reveal the worth of the proposed system’by obtaining a True Positive Rate of 98%with a 2%False Positive Rate. | Imran Ahmed Iqbal Khan Misbah Ahmad Awais Adnan Hanan Aljuaid | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 9 | A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction显示文摘The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various systems.Such as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects worldwide.Traffic congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the road.To address this overwhelming problem,in this article,a cloudbased intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy approach.The aim of the study is to reduce the delay in the queues,the vehicles experience at different road junctions across the city.The proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things(IoT)sensors across the road.After due preprocessing over the cloud server,the proposed approach makes use of this data by incorporating the neuro-fuzzy engine.Consequently,it possesses a high level of accuracy by means of intelligent decision making with minimum error rate.Simulation results reveal the accuracy of the proposed model as 98.72%during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%,95.84%,97.56%and 98.03%,respectively.As far as the training phase analysis is concerned,the proposed scheme exhibits 99.214% accuracy. The proposed prediction modelis a potential contribution towards smart cities environment. | Mohammed Gollapalli Atta-ur-Rahman Dhiaa Musleh Nehad Ibrahim Muhammad Adnan Khan Sagheer Abbas Ayesha Atta Muhammad Aftab Khan Mehwash Farooqui Tahir Iqbal Mohammed Salih Ahmed Mohammed Imran BAhmed Dakheel Almoqbil Majd Nabeel Abdullah Omer | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 10 | A Hierarchal Clustered Based Proactive Caching in NDN-Based Vehicular Network显示文摘An Information-Centric Network(ICN)provides a promising paradigm for the upcoming internet architecture,which will struggle with steady growth in data and changes in accessmodels.Various ICN architectures have been designed,including Named Data Networking(NDN),which is designed around content delivery instead of hosts.As data is the central part of the network.Therefore,NDN was developed to get rid of the dependency on IP addresses and provide content effectively.Mobility is one of the major research dimensions for this upcoming internet architecture.Some research has been carried out to solve the mobility issues,but it still has problems like handover delay and packet loss ratio during real-time video streaming in the case of consumer and producer mobility.To solve this issue,an efficient hierarchical Cluster Base Proactive Caching for Device Mobility Management(CB-PC-DMM)in NDN Vehicular Networks(NDN-VN)is proposed,through which the consumer receives the contents proactively after handover during the mobility of the consumer.When a consumer moves to the next destination,a handover interest is sent to the connected router,then the router multicasts the consumer’s desired data packet to the next hop of neighboring routers.Thus,once the handover process is completed,consumers can easily get the content to the newly connected router.A CB-PCDMM in NDN-VN is proposed that improves the packet delivery ratio and reduces the handover delay aswell as cluster overhead.Moreover,the intra and inter-domain handover handling procedures in CB-PC-DMM for NDN-VN have been described.For the validation of our proposed scheme,MATLAB simulations are conducted.The simulation results show that our proposed scheme reduces the handover delay and increases the consumer’s interest satisfaction ratio.The proposed scheme is compared with the existing stateof-the-art schemes,and the total percentage of handover delays is decreased by up to 0.1632%,0.3267%,2.3437%,2.3255%,and 3.7313%at the mobility speeds of 5 m/s,10 m/s,15 m/s,20 m/s,and 25 m/s,and the efficiency of the packet delivery ratio is improved by up to 1.2048%,5.0632%,6.4935%,6.943%,and 8.4507%.Furthermore,the simulation results of our proposed scheme show better efficiency in terms of Packet Delivery Ratio(PDR)from 0.071 to 0.077 and a decrease in the handover delay from 0.1334 to 0.129. | Muhammad Yasir Khan Muhammad Adnan Jawaid Iqbal Noor ul Amin Byeong-Hee Roh Jehad Ali | 2023 | Computer Systems Science & Engineering2023，47，10： | 0 |
| 11 | Fish as a source of acoustic signal measurement in an aquaculture tank:Acoustic sensor based time frequency analysis显示文摘Acoustic signals travels rapidly in water without attenuating fish telemetry.The digital sonar and passive acoustic has been used for fish monitoring and fish feeding.However,it is an urgent need to introduce new techniques in order to monitor the growth rate of fish during harvesting and without causing adverse effects to the harvested fish.Therefore,a novel technique was introduced to probe the acoustic signal frequency ratio in absence and presence of the fish in tanks,which basically uses an acoustic sensor(hydrophone),acoustic signal processing system(scope meter),and a signal monitoring system(fluke view).Acoustic signals were selected from 48-52 Hz frequency,measure of dispersion of frequency signal represented as a function of time via Xlstat software.Measure of dispersion displayed a significant effect of acoustic signal in the presence and absence of the fish in tanks.These optimised protocols of this study will help to control and prevent excessive wastage of feed and enhance proper utilization of feed that chiefly enhance fish growth in aquaculture. | Shahbaz Gul Hassan Shakeel Ahmad Shafqat Iqbal Ehsan Elahi Murtaza Hasan Daoliang Li Zhiyan Zhou Adnan Abbas Cancan Song | 2019 | International Journal of Agricultural and Biological Engineering2019，12，3： | 0 |
| 12 | 新型Al/CuFe多层核壳颗粒增强铝基复合材料的显微组织和力学性能显示文摘采用不同烧结工艺制备新型Al/Cu Fe多层核壳颗粒增强铝基复合材料(AMCs),研究其对显微组织和力学性能的影响。分别采用电置换法和化学镀法在铝粉颗粒上沉积Cu层和Fe层,制备多层Al/Cu Fe核壳颗粒。采用X射线衍射(XRD)、扫描电子显微镜(SEM)/能谱(EDX)、密度计、显微硬度和压缩试验等方法对复合材料进行表征。结果表明,在放电等离子烧结复合材料中,由于沉积层向金属间化合物相转变,界面反应程度提高,复合材料的相对密度(99.26%)、显微硬度(165 HV0.3)和强度(572 MPa)均显著提高。此外,热压复合材料壳层结构中存在的未转化Cu导致其断裂应变最高(20.4%)。研究结果对通过选择合适的烧结路径来调整AMCs的显微组织和控制其力学性能具有重要意义。 | Rashid ALI Fahad ALI Aqib ZAHOOR Rub Nawaz SHAHID Naeem ul Haq TARIQ Zafar IQBAL Adnan Qayyum BUTT Saad ULLAH Hasan Bin AWAIS | 2022 | Transactions of Nonferrous Metals Society of China2022，32，6： | 0 |
| 13 | Mobile Devices Interface Adaptivity Using Ontologies显示文摘Currently,many mobile devices provide various interaction styles and modes which create complexity in the usage of interfaces.The context offers the information base for the development of Adaptive user interface(AUI)frameworks to overcome the heterogeneity.For this purpose,the ontological modeling has been made for specific context and environment.This type of philosophy states to the relationship among elements(e.g.,classes,relations,or capacities etc.)with understandable satisfied representation.The contextmechanisms can be examined and understood by anymachine or computational framework with these formal definitions expressed in Web ontology language(WOL)/Resource description frame work(RDF).The Protégéis used to create taxonomy in which system is framed based on four contexts such as user,device,task and environment.Some competency questions and use-cases are utilized for knowledge obtaining while the information is refined through the instances of concerned parts of context tree.The consistency of the model has been verified through the reasoning software while SPARQL querying ensured the data availability in the models for defined use-cases.The semantic context model is focused to bring in the usage of adaptive environment.This exploration has finished up with a versatile,scalable and semantically verified context learning system.This model can be mapped to individual User interface(UI)display through smart calculations for versatile UIs. | Muhammad Waseem Iqbal Muhammad Raza Naqvi Muhammad Adnan Khan Faheem Khan T.Whangbo | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 14 | Hybridization of CNN with LBP for Classification of Melanoma Images显示文摘Skin cancer(melanoma)is one of the most aggressive of the cancers and the prevalence has significantly increased due to increased exposure to ultraviolet radiation.Therefore,timely detection and management of the lesion is a critical consideration in order to improve lifestyle and reduce mortality.To this end,we have designed,implemented and analyzed a hybrid approach entailing convolutional neural networks(CNN)and local binary patterns(LBP).The experiments have been performed on publicly accessible datasets ISIC 2017,2018 and 2019(HAM10000)with data augmentation for in-distribution generalization.As a novel contribution,the CNN architecture is enhanced with an intelligible layer,LBP,that extracts the pertinent visual patterns.Classification of Basal Cell Carcinoma,Actinic Keratosis,Melanoma and Squamous Cell Carcinoma has been evaluated on 8035 and 3494 cases for training and testing,respectively.Experimental outcomes with cross-validation depict a plausible performance with an average accuracy of 97.29%,sensitivity of 95.63%and specificity of 97.90%.Hence,the proposed approach can be used in research and clinical settings to provide second opinions,closely approximating experts’intuition. | Saeed Iqbal Adnan N.Qureshi Ghulam Mustafa | 2022 | Computers, Materials & Continua2022，，6： | 0 |
| 15 | MicroRNAs modulation in lung cancer: exploring dual mechanisms and clinical prospects显示文摘The global incidence of lung cancer is marked by a considerably elevated mortality rate.MicroRNAs(miRNAs)exert pivotal influence in the intricate orchestration of gene regulation,and their dysregulation can precipitate dire consequences,notably cancer.Within this context,miRNAs encapsulated in exosomes manifest a diversified impact on the landscape of lung cancer,wherein their actions may either foster angiogenesis,cell proliferation,and metastasis,or counteract these processes.This comprehensive review article discerns potential targets for the prospective development of therapeutic agents tailored for lung cancer.Tumor-suppressive miRNAs,such as miR-204,miR-192,miR-30a,miR-34a,miR-34b,miR-203,and miR-212,exhibit heightened expression and demonstrate the capacity to inhibit cellular proliferation and invasiveness.Conversely,the deleterious effects of tumor-promoting miRNAs like miR-21,miR-106a,miR-155,miR-205,and miR-210 can be attenuated through the application of their respective inhibitors.Distinct miRNAs selectively target various oncogenes,including NUAK Family Kinase 1(NUAK1),Snail Family Transcriptional Repressor 1(Snai1),Astrocyte elevated gene-1(AEG-1),Vimentin,Proliferation and apoptosis adaptor protein 15(PEA-15/PED),Hypoxia-inducible factor 1-alpha(HIF1),as well as tumor suppressor genes such as phosphatase and tensin homolog(PTEN),Suppressor of cytokine signaling 1(SOCS1),Tumor protein P53 binding protein 1(TP53BP1),and PH Domain and Leucine Rich Repeat Protein Phosphatase 2(PHLP22).This investigative approach proves invaluable in elucidating the specific miRNAs implicated in the deregulation of crucial genes pivotal to the pathogenesis of cancer. | SHAHID HUSSAIN HABIB BOKHARI XINGXING FAN SHAUKAT IQBAL MALIK SUNDAS IJAZ MUHAMMAD ADNAN SHEREEN AIMAN FATIMA | 2024 | BIOCELL2024，48，3： | 0 |
| 16 | National guidelines for the diagnosis and treatment of hilar cholangiocarcinoma显示文摘A consensus meeting of national experts from all major national hepatobiliary centres in the country was held on May 26,2023,at the Pakistan Kidney and Liver Institute&Research Centre(PKLI&RC)after initial consultations with the experts.The Pakistan Society for the Study of Liver Diseases(PSSLD)and PKLI&RC jointly organised this meeting.This effort was based on a comprehensive literature review to establish national practice guidelines for hilar cholangiocarcinoma(hCCA).The consensus was that hCCA is a complex disease and requires a multidisciplinary team approach to best manage these patients.This coordinated effort can minimise delays and give patients a chance for curative treatment and effective palliation.The diagnostic and staging workup includes high-quality computed tomography,magnetic resonance imaging,and magnetic resonance cholangiopancreato-graphy.Brush cytology or biopsy utilizing endoscopic retrograde cholangiopancreatography is a mainstay for diagnosis.However,histopathologic confirmation is not always required before resection.Endoscopic ultrasound with fine needle aspiration of regional lymph nodes and positron emission tomography scan are valuable adjuncts for staging.The only curative treatment is the surgical resection of the biliary tree based on the Bismuth-Corlette classification.Selected patients with unresectable hCCA can be considered for liver transplantation.Adjuvant chemotherapy should be offered to patients with a high risk of recurrence.The use of preoperative biliary drainage and the need for portal vein embolisation should be based on local multidisciplinary discussions.Patients with acute cholangitis can be drained with endoscopic or percutaneous biliary drainage.Palliative chemotherapy with cisplatin and gemcitabine has shown improved survival in patients with irresectable and recurrent hCCA. | Faisal Saud Dar Zaigham Abbas Irfan Ahmed Muhammad Atique Usman Iqbal Aujla Muhammad Azeemuddin Zeba Aziz Abu Bakar Hafeez Bhatti Tariq Ali Bangash Amna Subhan Butt Osama Tariq Butt Abdul Wahab Dogar Javed Iqbal Farooqi Faisal Hanif Jahanzaib Haider Siraj Haider Syed Mujahid Hassan Adnan Abdul Jabbar Aman Nawaz Khan Muhammad Shoaib Khan Muhammad Yasir Khan Amer Latif Nasir Hassan Luck Ahmad Karim Malik Kamran Rashid Sohail Rashid Mohammad Salih Abdullah Saeed Amjad Salamat Ghias-un-Nabi Tayyab Aasim Yusuf Haseeb Haider Zia Ammara Naveed | 2024 | World Journal of Gastroenterology2024，30，9： | 0 |
| 17 | Negotiation Based Combinatorial Double Auction Mechanism in Cloud Computing显示文摘Cloud computing is a demanding business platform for services related to the field of IT.The goal of cloud customers is to access resources at a sustainable price,while the goal of cloud suppliers is to maximize their services utilization.Previously,the customers would bid for every single resource type,which was a limitation of cloud resources allocation.To solve these issues,researchers have focused on a combinatorial auction in which the resources are offered by the providers in bundles so that the user bids for their required bundle.Still,in this allocation mechanism,some drawbacks need to be tackled,such as due to the lower average bid price the users are dropped from the auction process.To solve this problem,we proposed a“Negotiation based Combinatorial Double Auction Mechanism for Resource Allocation(N-CDARA)in cloud computing”.The proposed method negotiates with dropped users.Lower average bid price users are asked by our proposed mechanism to increase their bids,as by the quoted bids they will be dropped by the auctioneer.Most of the users that are close to winning accept the proposal and increase their bid prices.The proposed mechanism is implemented in a CloudSim simulation toolkit.Results are compared with the latest model and performance study shows that in our proposed scheme more users win and get their requested services and the utilization of offered services is increased up to 18.4%than the existing schemes. | Zakir Ullah Asif Umer Mahdi Zaree Jamil Ahmad Faisal Alanazi Noor Ul Amin Arif Iqbal Umar Ali Imran Jehangiri Muhammad Adnan | 2021 | Computers, Materials & Continua2021，，11： | 0 |
| 18 | A Fault-Handling Method for the Hamiltonian Cycle in the Hypercube Topology显示文摘Many routing protocols,such as distance vector and link-state protocols are used for nding the best paths in a network.To nd the path between the source and destination nodes where every node is visited once with no repeats,Hamiltonian and Hypercube routing protocols are often used.Nonetheless,these algorithms are not designed to solve the problem of a node failure,where one or more nodes become faulty.This paper proposes an efcient modied Fault-free Hamiltonian Cycle based on the Hypercube Topology(FHCHT)to perform a connection between nodes when one or more nodes become faulty.FHCHT can be applied in a different environment to transmit data with a high-reliability connection by nding an alternative path between the source and destination nodes when some nodes fail.Moreover,a proposed Hamiltonian Near Cycle(HNC)scheme has been developed and implemented.HNC implementation results indicated that FHCHT produces alternative cycles relatively similar to a Hamiltonian Cycle for the Hypercube,complete,and random graphs.The implementation of the proposed algorithm in a Hypercube achieved a 31%and 76%reduction in cost compared to the complete and random graphs,respectively. | Adnan A.Hnaif Abdelfatah A.Tamimi Ayman M.Abdalla Iqbal Jebril | 2021 | Computers, Materials & Continua2021，，7： | 0 |
| 19 | On the complexity of average path length for biological networks and patterns显示文摘 | Waqar Asif Hassaan Khaliq Qureshi Adnan Iqbal Muttukrishnan Rajarajan | 2014 | International Journal of Biomathematics2014，7，4： | 0 |
| 20 | Technical Analysis of Security Management in Terms of Crowd Energy and Smart Living显示文摘In this paper, a technical and statistical analysis of security system and security management is provided for crowd energy and smart living. At the same time, a clear understanding is made for crowd energy concept and next generation smart living. Various case examples have been studied and a brief summary has been provided.Furthermore, a statistical analysis has been provided in terms of security management in smart living where it is found that young technocrats give the highest importance to security management in smart living. Last but not the least, current limitation, constraints, and future scope of security implementation have been discussed in terms of crowd energy clustered with next generation smart living. | MD Shahrukh Adnan Khan Muhammad Ahad Rahman Miah Shaikh Rashedur Rahman Mirza Mursalin Iqbal Aseef Iqbal Aravind CV Chua Kein Huat | 2018 | Journal of Electronic Science and Technology2018，16，4： | 0 |
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