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Intelligent Service Search Model Using Emerging Technologies

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Computers, Materials & Continua 2023年第10期77卷 1165-1181页

作者： farhan amin Gyu Sang Choi Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

In recent years,the Internet of Things(IoT)has played a vital role in providing various services to users in a smart ***,searching for services,objects,data,and frameworks remains a *** technological advancements in Cyber-Physical Systems(CPSs)and the Social Internet of Things(SIoT)open a new era of ***,we propose a Cyber-Physical-Social Systems(CPSs)for service ***,service search and object discovery operation carries with the suitable selection of friends in the *** proposed model constructs a graph and performs social network analysis(SNA).We suggest degree centrality,clustering,and scalefree emergence and show that a rational selection of friends per service exploration increases the overall network *** efficiency of our proposed system is verified using real-world datasets based on service processing time,path length,giant component,and network *** simulation results proved that our proposed system is efficient,robust,and scalable.

关键词： Internet of Things Social Internet of Things Cyber-Physical Social systems smart cities service search smart cities

Model for Generating Scale-Free Artificial Social Networks Using Small-World Networks

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Computers, Materials & Continua 2022年第12期73卷 6367-6391页

作者： farhan amin Gyu Sang Choi Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

The Internet of Things(IoT)has the potential to be applied to social networks due to innovative characteristics and sophisticated solutions that challenge traditional *** network analysis(SNA)is a good example that has recently gained a lot of scientific *** has its roots in social and economic research,as well as the evaluation of network science,such as graph *** in this area have subverted predefined theories,offering revolutionary ones regarding interconnected networks,and they have highlighted the mystery of six degrees of separation with confirmation of the small-world *** motivation of this study is to understand and capture the clustering properties of large networks and social *** present a network growth model in this paper and build a scale-free artificial social network with controllable clustering *** random walk technique is paired with a triangle generating scheme in our proposed *** a result,the clustering controlmechanism and preferential attachment(PA)have been *** research builds on the present random walk *** took numerous measurements for validation,including degree behavior and the measure of clustering decay in terms of node degree,among other ***,we conclude that our suggested random walk model is more efficient and accurate than previous state-of-the-art methods,and hence it could be a viable alternative for societal evolution.

关键词： Social networks small-world networks network generation models graph theory random walk network design social network analysis

Automated Service Search Model for the Social Internet of Things

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Computers, Materials & Continua 2022年第9期72卷 5871-5888页

作者： farhan amin Seong Oun Hwang Department of Computer Engineering Gachon UniversitySeongnam13120Korea

The social internet of things(SIoT)is one of the emerging paradigms that was proposed to solve the problems of network service discovery,navigability,and service *** SIoT aims to socialize the IoT devices and shape the interconnection between them into social interaction just like human *** IoT,an object can offer multiple services and different objects can offer the same services with different parameters and interest *** proliferation of offered services led to difficulties during service customization and service *** problem is known as service *** selection of suitable service that fits the requirements of applications and objects is a challenging *** address these issues,we propose an efficient automated query-based service search model based on the local network navigability concept for the *** the proposed model,objects can use information from their friends or friends of their friends while searching for the desired services,rather than exploring a global *** employ a centrality metric that computes the degree of importance for each object in the social IoT that helps in selecting neighboring objects with high centrality *** distributed nature of our navigation model results in high scalability and short navigation *** verified the efficacy of our model on a real-world SIoT-related *** experimental results confirm the validity of our model in terms of scalability,navigability,and the desired objects that provide services are determined quickly via the shortest path,which in return improves the service search process in the SIoT.

关键词： Social internet of things service discovery local navigability object discovery query generation model

Advanced Community Identification Model for Social Networks

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Computers, Materials & Continua 2021年第11期69卷 1687-1707页

作者： farhan amin Jin-Ghoo Choi Gyu Sang Choi Department of Computer Engineering Gachon UniversityGyeonggi-do13120Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Community detection in social networks is a hard problem because of the size,and the need of a deep understanding of network structure and *** several methods with significant effort in this direction have been devised,an outstanding open problem is the unknown number of communities,it is generally believed that the role of influential nodes that are surrounded by neighbors is very *** addition,the similarity among nodes inside the same cluster is greater than among nodes from other ***,the global and local methods of community detection have been getting more ***,in this study,we propose an advanced communitydetection model for social networks in order to identify network communities based on global and local *** proposed model initially detects the most influential nodes by using an Eigen score then performs local expansion powered by label *** process is conducted with the same color till nodes reach maximum ***,the communities are formed,and a clear community graph is displayed to the *** proposed model is completely parameter-free,and therefore,no prior information is required,such as the number of communities,*** perform simulations and experiments using well-known synthetic and real network benchmarks,and compare them with well-known state-of-the-art *** results prove that our model is efficient in all aspects,because it quickly identifies communities in the ***,it can easily be used for friendship recommendations or in business recommendation systems.

关键词： Community detection social network analysis complex networks

Big Data Analytics Using Graph Signal Processing

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Computers, Materials & Continua 2023年第1期74卷 489-502页

作者： farhan amin Omar M.Barukab Gyu Sang Choi Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Computing and Information Technology-Rabigh King Abdulaziz UniversityJeddahSaudi Arabia

The networks are fundamental to our modern world and they appear throughout science and *** to a massive amount of data presents a unique opportunity to the researcher’s *** networks grow in size the complexity increases and our ability to analyze them using the current state of the art is at severe risk of failing to keep ***,this paper initiates a discussion on graph signal processing for large-scale data *** first provide a comprehensive overview of core ideas in Graph signal processing(GSP)and their connection to conventional digital signal processing(DSP).We then summarize recent developments in developing basic GSP tools,including methods for graph filtering or graph learning,graph signal,graph Fourier transform(GFT),spectrum,graph frequency,*** filtering is a basic task that allows for isolating the contribution of individual frequencies and therefore enables the removal of *** then consider a graph filter as a model that helps to extend the application of GSP methods to large *** show the suitability and the effeteness,we first created a noisy graph signal and then applied it to the *** several rounds of simulation *** see that the filtered signal appears to be smoother and is closer to the original noise-free distance-based *** using this example application,we thoroughly demonstrated that graph filtration is efficient for big data analytics.

关键词： Big data data science big data processing graph signal processing social networks

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

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Digital Communications and Networks 2024年第1期10卷 190-204页

作者： farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Math Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate *** terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,*** terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal *** is challenging to identify a specific attack due to complex features and data imbalance *** address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced ***,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,***,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic ***,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority ***,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network ***,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep *** experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and *** explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning Features extraction Imbalance data Explainable AI Cybersecurity

ROBO-SPOT:Detecting Robocalls by Understanding User Engagement and Connectivity Graph

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Big Data Mining and Analytics 2024年第2期7卷 340-356页

作者： Muhammad Ajmal Azad Junaid Arshad farhan Riaz College of Comnputer Science Birmingham City UniversityBirminghamB55JUUK School of Computer Science University of LincolnLincolnLN67TSUK

Robo or unsolicited calls have become a persistent issue in telecommunication networks,posing significant challenges to individuals,businesses,and regulatory *** calls not only trick users into disclosing their private and financial information,but also affect their productivity through unwanted phone ringing.A proactive approach to identify and block such unsolicited calls is essential to protect users and service providers from potential ***,this paper proposes a solution to identify robo-callers in the telephony network utilising a set of novel features to evaluate the trustworthiness of callers in a *** trust score of the callers is then used along with machine learning models to classify them as legitimate or *** use a large anonymized dataset(call detailed records)from a large telecommunication provider containing more than 1 billion records collected over 10 *** have conducted extensive evaluation demonstrating that the proposed approach achieves high accuracy and detection rate whilst minimizing the error ***,the proposed features when used collectively achieve a true-positive rate of around 97%with a false-positive rate of less than 0.01%.

关键词： social network analysis reputation unwanted calls robo-callers telephone network Spam Over Internet Technology(SPIT)

A Novel Hybrid Ensemble Learning Approach for Enhancing Accuracy and Sustainability in Wind Power Forecasting

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Computers, Materials & Continua 2024年第5期79卷 3373-3395页

作者： farhan Ullah Xuexia Zhang Mansoor Khan Muhammad Abid Abdullah Mohamed School of Electrical Engineering Southwest Jiaotong UniversityChengdu611756China College of Intelligent Manufacturing and Control Engineering Qilu Institute of TechnologyJinan250200China College of Aerospace and Civil Engineering Harbin Engineering UniversityHarbin150001China Research Centre Future University in EgyptNew Cairo11835Egypt

Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance *** approaches frequently struggle with complex data and non-linear connections. This article presentsa novel approach for hybrid ensemble learning that is based on rigorous requirements engineering *** approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-EraRetrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms usingin-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model,while a temporal convolutional network handles time-series complexities and data gaps. The ensemble-temporalneural network is enhanced by providing different input parameters including training layers, hidden and dropoutlayers along with activation and loss functions. The proposed framework is further analyzed by comparing stateof-the-art forecasting models in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE),respectively. The energy efficiency performance indicators showed that the proposed model demonstrates errorreduction percentages of approximately 16.67%, 28.57%, and 81.92% for MAE, and 38.46%, 17.65%, and 90.78%for RMSE for MERRAWind farms 1, 2, and 3, respectively, compared to other existingmethods. These quantitativeresults show the effectiveness of our proposed model with MAE values ranging from 0.0010 to 0.0156 and RMSEvalues ranging from 0.0014 to 0.0174. This work highlights the effectiveness of requirements engineering in windpower forecasting, leading to enhanced forecast accuracy and grid stability, ultimately paving the way for moresustainable energy solutions.

关键词： Ensemble learning machine learning real-time data analysis stakeholder analysis temporal convolutional network wind power forecasting

The role of predictive and prognostic values of inflammatory markers in acute pancreatitis:a narrative review

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Journal of Pancreatology 2024年第1期7卷 72-85页

作者： Saira Rafaqat Aqsa Sattar farhan Anjum Mahrukh Gilani Sana Rafaqat Department of Zoology(Molecular Physiology) Lahore College for Women UniversityLahorePakistan Institute of Zoology University of the PunjabQuaid-i-Azam CampusLahorePakistan Department of Zoology Lahore College for Women UniversityLahorePakistan Department of Biotechnology(Human Genetics) Lahore College for Women UniversityLahorePakistan

Pancreatitis is an inflammatory condition affecting the pancreas and is classified into 2 types,acute and chronic,which can manifest in various *** review article summarizes the role of predictive and prognostic values of inflammatory markers in the pathogenesis of acute pancreatitis,mainly focused on preclinical and clinical *** includes serum amyloid A(SAA),monocyte chemotactic protein-1(MCP-1),erythrocyte sedimentation rate(ESR),interleukin-6(IL-6),C-reactive protein(CRP),IL-10,myeloperoxidase,pentraxin 3,and plasminogen activator inhibitor ***3 plays a crucial role in developing acute pancreatitis by triggering a receptor-interacting protein 3-dependent necroptosis pathway in acinar *** SAA3 could be a potential strategy for treating acute *** recruitment of monocytes/macrophages and the activation of the systemic MCP-1 signaling pathway play a role in the progression of pancreatitis,and blocking MCP-1 may have a suppressive effect on the development of pancreatic *** ESR can predict severe acute pancreatitis with slightly lower accuracy than *** ESR and CRP levels are combined at 24 hours,they predict severe acute pancreatitis ***-6 plays a crucial role in activating the Janus kinase/signal transducers and activators of the transcription pathway,exacerbating pancreatitis and contributing to the initiation and progression of pancreatic *** IL-10 plays a crucial role in controlling the regenerative phase and limiting the severity of fibrosis and glandular atrophy induced by repeated episodes of acute pancreatitis in *** predictive and diagnostic roles of these inflammatory factors in pancreatitis were introduced in detail in this review.

关键词： Acute pancreatitis Inflammatory markers Pathogenesis