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| 1 | Enhancement of the oxidative stability of some vegetable oils by blending with Moringa oleifera oil显示文摘 | Farooq Anwar Abdullah Ijaz Hussain Shahid Iqbal Muhammad Iqbal Bhanger | 2006 | Food Chemistry2006，，4： | 1 |
| 2 | Field evaluation of Eimeria tenella (local isolates) gametocytes vaccine and its comparative efficacy with imported live vaccine, LivaCox?显示文摘 | M. Irfan Anwar Masood Akhtar Iftikhar Hussain A. U. Haq Faqir Muhammad M. Abdul Hafeez M. Shahid Mahmood Saira Bashir | 2008 | Parasitology Research2008，，1： | 1 |
| 3 | Measuring quality of experience for 360-degree videos in virtual reality显示文摘In recent years,we witness dramatic growing attention in immersive media technologies like 360-degree videos and virtual reality(VR).However,measuring the quality-of-experience(QoE)for 360-degree VR videos is not a trivial task.Streaming such videos to head mounted displays(HMDs)is extremely bandwidth-demanding when compared to traditional 2D videos.In HTTP adaptive streaming,QoE tends to deteriorate significantly during fluctuating network conditions,which results in various bitrate changes and causes multiple stalling events during playback.Thus,understanding how the human visual system perceives 360-degree video with the effect of stalling and different bitrate levels becomes inevitable.In this paper,we investigate the impact of stalling on users QoE under different bitrate levels and the interaction between stalling event and bitrate level for 360-degree videos in VR.To aim this,we first build a 360-degree videos database by encoding videos in three different bitrate levels(1,5,and 15 Mbps)with 4 K resolutions(3840×1920 pixels).We then simulate various stalling events in the videos and conduct a subjective experiment in a virtual reality environment to investigate the human responses.Finally,we use a Bayesian method to estimate and predict the QoE while measuring the quality drop owing to various stalling events and bitrate changes.Proposed solution and prediction results show a strong dependency between playback stalling and bitrate of 360-degree video in VR.Stalling always impacts the QoE of 360-degree videos,but the strength of this negative impact depends on the video bitrate level.The adverse effect of stalling events is more profound when bitrate level approaches to the high and low end,which is in close agreement with subjective opinion. | Muhammad Shahid ANWAR Jing WANG Asad ULLAH Wahab KHAN Sadique AHMAD Zesong FEI | 2020 | Science China(Information Sciences)2020，63，10： | 1 |
| 4 | Green Synthesis of Silver Nanoparticles: Structural Features and In Vivo and In Vitro Therapeutic Effects against Helicobacter pylori Induced Gastritis显示文摘 | Muhammad Amin Sadaf Hameed Asghar Ali Farooq Anwar Shaukat Ali Shahid Imran Shakir Aqdas Yaqoob Sara Hasan Safyan Akram Khan Sajjad-ur-Rahman Imre Sovago | 2014 | Bioinorganic Chemistry and Applications2014，，： | 1 |
| 5 | Attention-based neural network for end-to-end music separation显示文摘The end-to-end separation algorithm with superior performance in the field of speech separation has not been effectively used in music separation.Moreover,since music signals are often dual channel data with a high sampling rate,how to model longsequence data and make rational use of the relevant information between channels is also an urgent problem to be solved.In order to solve the above problems,the performance of the end-to-end music separation algorithm is enhanced by improving the network structure.Our main contributions include the following:(1)A more reasonable densely connected U-Net is designed to capture the long-term characteristics of music,such as main melody,tone and so on.(2)On this basis,the multi-head attention and dualpath transformer are introduced in the separation module.Channel attention units are applied recursively on the feature map of each layer of the network,enabling the network to perform long-sequence separation.Experimental results show that after the introduction of the channel attention,the performance of the proposed algorithm has a stable improvement compared with the baseline system.On the MUSDB18 dataset,the average score of the separated audio exceeds that of the current best-performing music separation algorithm based on the time-frequency domain(T-F domain). | Jing Wang Hanyue Liu Haorong Ying Chuhan Qiu Jingxin Li Muhammad Shahid Anwar | 2023 | CAAI Transactions on Intelligence Technology2023，8，2： | 0 |
| 6 | Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques显示文摘Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment. | Nasser Alshammari Shumaila Shahzadi Saad Awadh Alanazi Shahid Naseem Muhammad Anwar Madallah Alruwaili Muhammad Rizwan Abid Omar Alruwaili Ahmed Alsayat Fahad Ahmad | 2024 | Computer Systems Science & Engineering2024，48，2： | 0 |
| 7 | Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT显示文摘Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems. | Muhammad Tahir Mingchu Li Irfan Khan Salman AAl Qahtani Rubia Fatima Javed Ali Khan Muhammad Shahid Anwar | 2023 | Computers, Materials & Continua2023，77，11： | 0 |
| 8 | Abundance and Breeding of the Common Skittering Frog(Euphlyctis cyanophlyctis)and Bull Frog(Hoplobatrachus tigerinus)at Rawal Lake,Islamabad,Pakistan显示文摘The population density and breeding of two frog species,i.e.,the Skittering Frog(Euphlyctis cyanophlyctis)and the Bull Frog(Hoplobatrachus tigerinus)were studied at Rawal Lake,Islamabad,Pakistan,by using visual encounter method from September 2009 to August 2010.Mean population density for the two frogs was 1.55±0.44frogs per ha,with that for the Skittering Frog being 1.09±0.33 frogs/ha and that of the Bull Frog 0.46±0.11 frog/ha,respectively.It is concluded that both frog species are explosive breeders,i.e.,their breeding activities were confi ned to the f irst showers of the monsoon season.The mean spawn weight of the Skittering Frog was 1.5 g with more than 1000eggs in each spawn,while that of the Bull Frog was 0.26 g with less than 1000 eggs in a spawn.The spawning sites of the two species were investigated in detail.Generally,no threat to their populations was observed.However,the opening of the spillway of Rawal Dam following the torrential rain destroyed the breeding sites of the frogs.It is maintained that the event did not produce any signif icant impact on their populations and breeding as the two species were quite common. | Fozia TABASSUM Muhammad RAIS Maqsood ANWAR Tariq MEHMOOD Iftikhar HUSSAIN Shahid ALI KHAN | 2011 | Asian Herpetological Research2011，2，4： | 0 |
| 9 | Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features显示文摘Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode. | Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar | 2024 | Computers, Materials & Continua2024，78，3： | 0 |
| 10 | NPBMT: A Novel and Proficient Buffer Management Technique for Internet of Vehicle-Based DTNs显示文摘Delay Tolerant Networks(DTNs)have the major problem of message delay in the network due to a lack of endto-end connectivity between the nodes,especially when the nodes are mobile.The nodes in DTNs have limited buffer storage for storing delayed messages.This instantaneous sharing of data creates a low buffer/shortage problem.Consequently,buffer congestion would occur and there would be no more space available in the buffer for the upcoming messages.To address this problem a buffer management policy is proposed named“A Novel and Proficient Buffer Management Technique(NPBMT)for the Internet of Vehicle-Based DTNs”.NPBMT combines appropriate-size messages with the lowest Time-to-Live(TTL)and then drops a combination of the appropriate messages to accommodate the newly arrived messages.To evaluate the performance of the proposed technique comparison is done with Drop Oldest(DOL),Size Aware Drop(SAD),and Drop Larges(DLA).The proposed technique is implemented in the Opportunistic Network Environment(ONE)simulator.The shortest path mapbased movement model has been used as the movement path model for the nodes with the epidemic routing protocol.From the simulation results,a significant change has been observed in the delivery probability as the proposed policy delivered 380 messages,DOL delivered 186 messages,SAD delivered 190 messages,and DLA delivered only 95 messages.A significant decrease has been observed in the overhead ratio,as the SAD overhead ratio is 324.37,DLA overhead ratio is 266.74,and DOL and NPBMT overhead ratios are 141.89 and 52.85,respectively,which reveals a significant reduction of overhead ratio in NPBMT as compared to existing policies.The network latency average of DOL is 7785.5,DLA is 5898.42,and SAD is 5789.43 whereas the NPBMT latency average is 3909.4.This reveals that the proposed policy keeps the messages for a short time in the network,which reduces the overhead ratio. | Sikandar Khan Khalid Saeed Muhammad Faran Majeed Salman A.AlQahtani Khursheed Aurangzeb Muhammad Shahid Anwar | 2023 | Computers, Materials & Continua2023，77，10： | 0 |
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