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Development of a Smart Technique for Mobile Web Services Discovery

Computers, Materials & Continua 2021年第11期69卷 1483-1501页

作者： Mohamed Eb-saad Yunyoung Nam Hazem M.El-bakry Samir Abdelrazek Department of Information Systems Faculty of Computers and InformationMansoura UniversityMansoura35516Egypt Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Web service(WS)presents a good solution to the interoperability of different types of systems that aims to reduce the overhead of high processing in a resource-limited *** the increasing demand for mobile WS(MWS),the WS discovery process has become a significant challenging point in the WS lifecycle that aims to identify the relevant MWSs that best match the service *** discovery process is a resource-consuming task that cannot be performed efficiently in a mobile computing environment due to the limitations of mobile ***,a cloud computing can provide rich computing resources for mobile environments given its unlimited and easily scalable *** paper proposes a semantic WS discovery and invocation framework in mobile environments based on cloud and a relationship-aware matchmaking *** discovery algorithm enriches MWS and user requests semantically with the functional and non-functional properties of Ontology Web Language for Services,such as Quality of Web Service,device context,and user *** WS repository is filtered based on logical reasoning and a parameter-based matching algorithm to minimize the matching space and improve runtime *** cosine similarity between the user request and services repository is then assessed to generate the most relevant *** relationships among concepts in the ontology are considered to improve the recall and precision *** the WS discovery process,users can invoke and test these services in a mobile environment through a dynamic user *** interface of the invocation process is changed according to the WS description *** application prototype is also developed to evaluate the framework based on a Cordova cross-mobile development framework.

关键词： Cloud web service web service discovery semantic matching mobile web service discovery

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Signature of the 27-day variation in hemispheric sunspot activity and asymmetry during 2010-2015

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Research in Astronomy and Astrophysics 2021年第4期21卷 331-340页

作者： Prithvi Raj Singh Ahmad Islam saad Farid Tarun Kumar Pant Abhay Kumar Singh Department of Physics Banaras Hindu UniversityVaranasi-221005India Physics and Chemistry Department Faculty of EducationAlexandria UniversityAlexandria-21526Egypt Space Physics Laboratory VSSCThiruvananthapuram-695022India

In the present work,we study the time evolution,significance of the N-S asymmetry excesses presented as a function of the solar cycle and prominent rotational periods(~27 d)separately for the northern and southern *** have investigated short-term variations of the hemispheric solar activity(sunspot numbers and sunspot areas)during the time period 2010-2015,which covers the ascending and the maximum phase of solar cycle *** have implemented the Lomb-Scargle periodogram and continuous wavelet transform power spectrum techniques to study the time evolution and dominant rotational periods separately for the northern and southern hemispheres,and whole solar *** results showed that the northern hemisphere exhibited longer solar synodic periods than the southern hemisphere,indicating that the northern hemisphere has a lower rotation ***,the northern hemisphere was found to be dominant before transferring to the southern hemisphere during ***,the sunspot areas clearly demonstrated a two-peak structure of solar activity in the northern and southern hemispheres respectively during 2012 and *** statistical significance of the southern hemisphere affirmed enhanced excess during the maximum phase of solar cycle 24.

关键词： Sunspots Sunspot Area Asymmetry Solar Cycle

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Threefold Optimized Forecasting of Electricity Consumption in Higher Education Institutions

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Computers, Materials & Continua 2022年第11期73卷 2351-2370页

作者： Majida Kazmi Hashim Raza Khan Lubaba Mohammad Hashir Bin Khalid saad Ahmed Qazi Faculty of Electrical and Computer Engineering NED University of Engineering and TechnologyKarachi75270Pakistan Neurocomputation Lab National Center of Artificial IntelligenceKarachi75270Pakistan

Energy management benefits both consumers and utility companiesalike. Utility companies remain interested in identifying and reducing energywaste and theft, whereas consumers’ interest remain in lowering their energyexpenses. A large supply-demand gap of over 6 GW exists in Pakistan asreported in 2018. Reducing this gap from the supply side is an expensiveand complex task. However, efficient energy management and distributionon demand side has potential to reduce this gap economically. Electricityload forecasting models are increasingly used by energy managers in takingreal-time tactical decisions to ensure efficient use of resources. Advancementin Machine-learning (ML) technology has enabled accurate forecasting ofelectricity consumption. However, the impact of computation cost affordedby these ML models is often ignored in favour of accuracy. This studyconsiders both accuracy and computation cost as concurrently significantfactors because together they shape the technology environment as well ascreate economic impact. Thus, a three-fold optimized load forecasting modelis proposed which includes (1) application specific parameters selection, (2)impact of different dataset granularities and (3) implementation of specificdata preparation. It deploys and compares the widely used back-propagationArtificial Neural Network (ANN) and Random Forest (RF) models for theprediction of electricity consumption of buildings within a university. In addition to the temporal and historical power consumption date as input parameters, the study also embeds weather data as well as university operationalcalendars resulting in improved performance. The outcomes are indicativethat the granularity i.e. the scale of details in data, and set of reduced and fullinput parameters impact performance accuracies differently for ANN and RFmodels. Experimental results show that overall RF model performed betterboth in terms of accuracy as well as computational time for a 1-min, 15-minand 1-h dataset

关键词： Electricity forecasting short term higher educational institution artificial neural network random forest accuracy computational time

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Wolbachia pipientis:A potential candidate for combating and eradicating dengue epidemics in Pakistan

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Asian Pacific Journal of Tropical Medicine 2015年第12期8卷 963-973页

作者： Uruj Tahir Umair Hassan Khan Muhammad saad Zubair Bahar-e-Mustafa Department of Environmental Sciences Fatima Jinnah Women University Department of Microbiology.University of Agriculture Faisalabad Department of Microbiology.Government College University Faisalabad

Dengue virus syndrome is an emerging global health challenge which is endemic in tropical countries like *** recent years dengue incidences have increased considerably in different areas of Pakistan with more sever impacts on urban and peri-urban *** review is an effort to highlight the changing epidemiology of dengue fever,role of Government of Pakistan in disease management and control using preventive and community based approaches in the ***,there is an emphasis on application of Wolbachia as novel,inexpeasive and environmentally benign candidate for control and eradication of dengue transmitting vectors.

关键词： Dengue Pakistan Antiviral agents Chemical methods

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Influences of processing parameters and heat treatment on microstructure and mechanical behavior of Ti-6Al-4V fabricated using selective laser melting

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Advances in Manufacturing 2022年第4期10卷 520-540页

作者： Qi-Dong Sun Jie Sun Kai Guo saad Waqar Jiang-Wei Liu Lei-Shuo Wang Key Laboratory of High Efficiency and Clean Mechanical Manufacture National Demonstration Center for Experimental Mechanical Engineering EducationSchool of Mechanical EngineeringShandong UniversityJinan250061People’s Republic of China Research Centre for Aeronautical Component Manufacturing Technology and Equipment Shandong UniversityJinan250061People’s Republic of China School of Energy and Power Engineering Shandong UniversityJinan250061People’s Republic of China School of Mechanical and Power Engineering Zhengzhou UniversityZhengzhou450001People’s Republic of China

Selective laser melting(SLM)has provided an alternative to the conventional fabrication techniques for Ti-6Al-4V alloy parts because of its flexibility and ease in creating complex ***,this study investigated the effects of the process parameters and heat treatment on the microstructure and mechanical properties of Ti-6Al-4V fabricated using *** influences of various process parameters on the relative density,tensile properties,impact toughness,and hardness of Ti-6Al-4V alloy parts were *** employing parameter optimization,a high-density high-strength Ti-6Al-4V alloy was fabricated by SLM.A relative density of 99.45%,a tensile strength of 1188 MPa,and an elongation to failure of 9.5%were achieved for the SLM-fabricated Ti-6Al-4V alloy with optimized *** effects of annealing and solution aging heat treatment on the mechanical properties,phase composition,and microstructure of the SLM-fabricated Ti-6Al-4V alloy were also *** ductility of the heat-treated Ti-6Al-4V alloy was *** applying a heat treatment at 850℃ for 2 h,followed by furnace cooling,the elongation to failure and impact toughness were found to be increased from 9.5%to 12.5%,and from 24.13 J/cm^(2)to 47.51 J/cm^(2),respectively.

关键词： Selective laser melting(SLM) Ti-6Al-4V alloy Heat treatment Microstructure Mechanical properties

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An Improved Empirical Mode Decomposition for Power Analysis Attack

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China Communications 2017年第9期14卷 94-99页

作者： Han Gan Hongxin Zhang Muhammad saad khan Xueli Wang Fan Zhang Pengfei He School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing Key Laboratory of Work Safety Intelligent Monitoring Beijing University of Posts and Telecommunications School of Science Beijing University of Posts and Telecommunications College of Information Science and Electrical Engineering Zhejiang University Institute of Science and Technology for Opto-electronic Information Yantai University

Correlation power analysis(CPA) has become a successful attack method about crypto-graphic hardware to recover the secret keys. However, the noise influence caused by the random process interrupts(RPIs) becomes an important factor of the power analysis attack efficiency, which will cost more traces or attack time. To address the issue, an improved method about empirical mode decomposition(EMD) was proposed. Instead of restructuring the decomposed signals of intrinsic mode functions(IMFs), we extract a certain intrinsic mode function(IMF) as new feature signal for CPA attack. Meantime, a new attack assessment is proposed to compare the attack effectiveness of different methods. The experiment shows that our method has more excellent performance on CPA than others. The first and the second IMF can be chosen as two optimal feature signals in CPA. In the new method, the signals of the first IMF increase peak visibility by 64% than those of the tradition EMD method in the situation of non-noise. On the condition of different noise interference, the orders of attack efficiencies are also same. With external noise interference, the attack effect of the first IMF based on noise with 15dB is the best.

关键词： power analysis attack, EMD,IMF, correlation power analysis, RPIs

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Privacy Preservation in IoT Devices by Detecting Obfuscated Malware Using Wide Residual Network

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Computers, Materials & Continua 2024年第11期81卷 2395-2436页

作者： Deema Alsekait Mohammed Zakariah Syed Umar Amin Zafar Iqbal Khan Jehad saad Alqurni Applied College Department of Computer Science and Information TechnologyPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadh11362Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Educational Technologies College of EducationImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus ***,the study suggests using an advanced approach that utilizes machine learning,specifically the Wide Residual Network(WRN),to identify hidden malware in IoT *** research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices,using the MalMemAnalysis ***,thorough experimentation provides evidence for the effectiveness of the WRN-based strategy,resulting in exceptional performance measures such as accuracy,precision,F1-score,and *** study of the test data demonstrates highly impressive results,with a multiclass accuracy surpassing 99.97%and a binary class accuracy beyond 99.98%.The results emphasize the strength and dependability of using advanced deep learning methods such as WRN for identifying hidden malware risks in IoT ***,a comparison examination with the current body of literature emphasizes the originality and efficacy of the suggested *** research builds upon previous studies that have investigated several machine learning methods for detecting malware on IoT ***,it distinguishes itself by showcasing exceptional performance metrics and validating its findings through thorough experimentation with real-world *** WRN offers benefits in managing the intricacies of malware detection,emphasizing its capacity to enhance the security of IoT *** summarize,this work proposes an effective way to address privacy concerns on IoT devices by utilizing advanced machine learning *** research provides useful insights into the changing landscape of IoT cybersecurity by emphasizing methodological rigor and conducting comparative performance *** research co

关键词： Obfuscated malware detection IoT devices Wide Residual Network(WRN) malware detection machine learning

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Social Distancing and Isolation Management Using Machine-to-Machine Technologies to Prevent Pandemics

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Computers, Materials & Continua 2021年第6期67卷 3545-3562页

作者： Muhammad saad Maaz Bin Ahmad Muhammad Asif Khalid Masood Mohammad A.Al Ghamdi College of Computing and Information Sciences PAF KIETKarachiPakistan Department of Computer Science Lahore Garrison UniversityLahorePakistan Department of Computer Science Umm Al-Qura UniversityMakkah CitySaudi Arabia

Social distancing and self-isolation management are crucial preventive measures that can save millions of lives during challenging pandemics of diseases such as the Spanish u,swine u,and coronavirus disease 2019(COVID-19).This study describes the comprehensive and effective implementation of the Industrial Internet of Things and machine-to-machine technologies for social distancing and smart self-isolation *** technologies can help prevent outbreaks of any disease that can disperse widely and develop into a ***,a smart wristband is proposed that incorporates Bluetooth beacon technology to facilitate the tracing and tracking of Bluetooth Low Energy beacon packets for smart contact ***,the connectivity of the device with Android or iOS applications using long-term evolution technology is realized to achieve ***,mathematical formulations are proposed to measure the distance between coordinates in order to detect geo-fencing *** formulations are specically designed for the virtual circular and polygonal boundaries used to restrict suspected or infected persons from trespassing in predetermined areas,e.g.,at home,in a hospital,or in an isolation *** proposed framework outperforms existing solutions,since it is implemented on a wider scale,provides a range of functionalities,and is cost-effective.

关键词： Coronavirus disease 2019 pandemic machine-to-machine industrial internet of things social distance geo-fencing

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Fake News Encoder Classifier (FNEC) for Online Published News Related to COVID-19 Vaccines

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Intelligent Automation & Soft Computing 2023年第7期37卷 73-90页

作者： Asma Qaiser Saman Hina Abdul Karim Kazi saad Ahmed Raheela Asif Department of Computer Science and Information Technology NED University of Engineering and TechnologyKarachi75270Pakistan Department of Computer Science Iqra UniversityKarachi76400Pakistan Department of Software Engineering NED University of Engineering and TechnologyKarachi75270Pakistan

In the past few years,social media and online news platforms have played an essential role in distributing news content *** of the authenticity of news has become a major *** the COVID-19 outbreak,misinformation and fake news were major sources of confusion and insecurity among the general *** the first quarter of the year 2020,around 800 people died due to fake news relevant to *** major goal of this research was to discover the best learning model for achieving high accuracy and performance.A novel case study of the Fake News Classification using ELECTRA model,which achieved 85.11%accuracy score,is thus reported in this *** addition to that,a new novel dataset called COVAX-Reality containing COVID-19 vaccine-related news has been *** the COVAX-Reality dataset,the performance of FNEC is compared to several traditional learning models i.e.,Support Vector Machine(SVM),Naive Bayes(NB),Passive Aggressive Classifier(PAC),Long Short-Term Memory(LSTM),Bi-directional LSTM(Bi-LSTM)and Bi-directional Encoder Representations from Transformers(BERT).For the evaluation of FNEC,standard metrics(Precision,Recall,Accuracy,and F1-Score)were utilized.

关键词： Deep learning fake news detection machine learning transformer model classification

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A Sustainable WSN System with Heuristic Schemes in IIoT

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Computers, Materials & Continua 2022年第9期72卷 4215-4231页

作者： Wenjun Li Siyang Zhang Guangwei Wu Aldosary saad Amr Tolba Gwang-jun Kim School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China College of Computer and Information Engineering Central South University of Forestry and TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Mathematics and Computer Science Department Faculty of ScienceMenoufia UniversityEgypt Department of Computer Engineering Chonnam National UniversityGwangju61186Korea

Recently, the development of Industrial Internet of Things hastaken the advantage of 5G network to be more powerful and more ***, the upgrading of 5G network will cause a variety of issues increase,one of them is the increased cost of coverage. In this paper, we proposea sustainable wireless sensor networks system, which avoids the problemsbrought by 5G network system to some extent. In this system, deployingrelays and selecting routing are for the sake of communication and *** main aim is to minimize the total energy-cost of communication underthe precondition, where each terminal with low-power should be charged byat least one relay. Furthermore, from the perspective of graph theory, weextract a combinatorial optimization problem from this system. After that,as to four different cases, there are corresponding different versions of theproblem. We give the proofs of computational complexity for these problems,and two heuristic algorithms for one of them are proposed. Finally, theextensive experiments compare and demonstrate the performances of thesetwo algorithms.

关键词： Industrial Internet of Things sustainable wireless sensor network system combinatorial optimization problem heuristic algorithms

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