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| 1 | Optimizing Energy Conservation in V2X Communications for 5G Networks显示文摘The smart vehicles are one of critical enablers for automated services in smart cities to provide intelligent transportation means without human intervention.In order to fulfil requirements,Vehicle-to-Anything(V2X)communications aims to manage massive connectivity and high traffic load on base stations and extend the range over multiple hops in 5G networks.However,V2X networking faces several challenges from dynamic topology caused by high velocity of nodes and routing overhead that degrades the network performance and increases energy consumption.The existing routing scheme for V2X networking lacks energy efficiency and scalability for high velocity nodes with dense distribution.In order to handle the challenges,this article proposes a scalable and energy-efficient routing scheme called Dynamic proactive reactive routing for 5G(DPR5)for high mobility speed and dense environment.As compared to existing schemes it uses a single routing table and efficiently minimizes the energy consumption in dense environment,improves node‘s lifetime upto 42%,and optimizes network performance by reducing the packet loss ratio upto 46%in a high velocity dense environment. | Arif Husen Abid Soahil Mohammad Hijji Muhammad Hasanain Chaudary Farooq Ahmed 无 | 2022 | Computers, Materials & Continua2022，，5： | 0 |
| 2 | Multilingual Sentiment Mining System to Prognosticate Governance显示文摘In the age of the internet,social media are connecting us all at the tip of our fingers.People are linkedthrough different social media.The social network,Twitter,allows people to tweet their thoughts on any particular event or a specific political body which provides us with a diverse range of political insights.This paper serves the purpose of text processing of a multilingual dataset including Urdu,English,and Roman Urdu.Explore machine learning solutions for sentiment analysis and train models,collect the data on government from Twitter,apply sentiment analysis,and provide a python library that classifies text sentiment.Training data contained tweets in three languages:English:200k,Urdu:200k and Roman Urdu:11k.Five different classification models are applied to determine sentiments,and eventually,the use of ensemble technique to move forward with the acquired results is explored.The Logistic Regression model performed best with an accuracy of 75%,followed by the Linear Support Vector classifier and Stochastic Gradient Descent model,both having 74%accuracy.Lastly,Multinomial Naïve Bayes and Complement Naïve Bayes models both achieved 73%accuracy. | Muhammad Shahid Bhatti Saman Azhar Abid Sohail Mohammad Hijji Hamna Ayemen Areesha Ramzan | 2022 | Computers, Materials & Continua2022，，4： | 0 |
| 3 | Defending Adversarial Examples by a Clipped Residual U-Net Model显示文摘Deep learning-based systems have succeeded in many computer vision tasks.However,it is found that the latest study indicates that these systems are in danger in the presence of adversarial attacks.These attacks can quickly spoil deep learning models,e.g.,different convolutional neural networks(CNNs),used in various computer vision tasks from image classification to object detection.The adversarial examples are carefully designed by injecting a slight perturbation into the clean images.The proposed CRU-Net defense model is inspired by state-of-the-art defense mechanisms such as MagNet defense,Generative Adversarial Net-work Defense,Deep Regret Analytic Generative Adversarial Networks Defense,Deep Denoising Sparse Autoencoder Defense,and Condtional Generattive Adversarial Network Defense.We have experimentally proved that our approach is better than previous defensive techniques.Our proposed CRU-Net model maps the adversarial image examples into clean images by eliminating the adversarial perturbation.The proposed defensive approach is based on residual and U-Net learning.Many experiments are done on the datasets MNIST and CIFAR10 to prove that our proposed CRU-Net defense model prevents adversarial example attacks in WhiteBox and BlackBox settings and improves the robustness of the deep learning algorithms especially in the computer visionfield.We have also reported similarity(SSIM and PSNR)between the original and restored clean image examples by the proposed CRU-Net defense model. | Kazim Ali Adnan N.Qureshi Muhammad Shahid Bhatti Abid Sohail Mohammad Hijji | 2023 | Intelligent Automation & Soft Computing2023，，2： | 0 |
| 4 | Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases显示文摘Machine Learning(ML)has changed clinical diagnostic procedures drastically.Especially in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human errors.Enormous studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical procedures.Multi-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical diagnostics.DL formulates reliable accuracy with big datasets but the reverse is the case with small datasets.This paper proposed a novel method that deals with the issue of less data dimensionality.Inspired by the regression analysis,the proposed method classifies the data by going through three different stages.In the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final classifications.Extensive experiments were carried out on the Cleveland heart disease dataset.The results show significant improvement in classification accuracy.It is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future. | Wasif Akbar Adbul Mannan Qaisar Shaheen Mohammad Hijji Muhammad Anwar Muhammad Ayaz | 2023 | Computers, Materials & Continua2023，，6： | 0 |
| 5 | Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers显示文摘Data centers are being distributed worldwide by cloud service providers(CSPs)to save energy costs through efficient workload alloca-tion strategies.Many CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload pro-cessing.Numerous research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers(DCs).However,oper-ating cost savings during workload processing,which also considers string-matching techniques in geo-distributed DCs,remains unexplored.In this research,we propose a novel string matching-based geographical load balanc-ing(SMGLB)technique to mitigate the operating cost of the geo-distributed DC.The primary goal of this study is to use a string-matching algorithm(i.e.,Boyer Moore)to compare the contents of incoming workloads to those of documents that have already been processed in a data center.A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save energy.On the contrary,if no match can be discovered,the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices,the number of active servers,on-site green energy,and traces of incoming workload.The results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques. | Muhammad Imran Khan Khalil Syed Adeel Ali Shah Izaz Ahmad Khan Mohammad Hijji Muhammad Shiraz Qaisar Shaheen | 2023 | Computers, Materials & Continua2023，，6： | 0 |
| 6 | De-Noising Brain MRI Images by Mixing Concatenation and Residual Learning(MCR)显示文摘Brain magnetic resonance images(MRI)are used to diagnose the different diseases of the brain,such as swelling and tumor detection.The quality of the brain MR images is degraded by different noises,usually salt&pepper and Gaussian noises,which are added to the MR images during the acquisition process.In the presence of these noises,medical experts are facing problems in diagnosing diseases from noisy brain MR images.Therefore,we have proposed a de-noising method by mixing concatenation,and residual deep learning techniques called the MCR de-noising method.Our proposed MCR method is to eliminate salt&pepper and gaussian noises as much as possible from the brain MRI images.The MCR method has been trained and tested on the noise quantity levels 2%to 20%for both salt&pepper and gaussian noise.The experiments have been done on publically available brain MRI image datasets,which can easily be accessible in the experiments and result section.The Structure Similarity Index Measure(SSIM)and Peak Signal-to-Noise Ratio(PSNR)calculate the similarity score between the denoised images by the proposed MCR method and the original clean images.Also,the Mean Squared Error(MSE)measures the error or difference between generated denoised and the original images.The proposed MCR denoising method has a 0.9763 SSIM score,84.3182 PSNR,and 0.0004 MSE for salt&pepper noise;similarly,0.7402 SSIM score,72.7601 PSNR,and 0.0041 MSE for Gaussian noise at the highest level of 20%noise.In the end,we have compared the MCR method with the state-of-the-art de-noising filters such as median and wiener de-noising filters. | Kazim Ali Adnan N.Qureshi Muhammad Shahid Bhatti Abid Sohail Muhammad Hijji Atif Saeed | 2023 | Computer Systems Science & Engineering2023，45，5： | 0 |
| 7 | Competency Driven Resource Evaluation Method for Business Process Intelligence显示文摘Enterprises are continuously aiming at improving the execution of processes to achieve a competitive edge.One of the established ways of improving process performance is to assign the most appropriate resources to each task of the process.However,evaluations of business process improvement approaches have established that a method that can guide decision-makers to identify the most appropriate resources for a task of process improvement in a structured way,is missing.It is because the relationship between resources and tasks is less understood and advancement in business process intelligence is also ignored.To address this problem an integrated resource classification framework is presenting that identifies competence,suitability,and preference as the relationship of task with resources.But,only the competence relationship of human resources with a task is presented in this research as a resource competence model.Furthermore,the competency calculation method is presented as a user guider layer for business process intelligencebased resource competence evaluation.The computed capabilities serve as a basic input for choosing the most appropriate resources for each task of the process.Applicability of method is illustrated through a heathcare case study. | Abid Sohail Dhanapal Durai Dominic Mohammad Hijji Muhammad Arif Butt | 2021 | Computers, Materials & Continua2021，，10： | 0 |
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