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Ontology-Based News Linking for Semantic Temporal Queries

computers, Materials & Continua 2023年第2期74卷 3913-3929页

作者： Muhammad Islam Satti Jawad Ahmed Hafiz Syed Muhammad Muslim Akber Abid Gardezi Shafiq Ahmad Abdelaty Edrees Sayed Salman Naseer Muhammad Shafiq Faculty of Computing Riphah International UniversityI-14 CampusIslamabad44000Pakistan Department of Computer Science Zabsolutions SZABISTIslamabadPakistan Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Information Technology University of the Punjab Gujranwala CampusGujranwala52250Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Daily newspapers publish a tremendous amount of information disseminated through the *** available and easily accessible large online repositories are not indexed and are in an un-processable *** major hindrance in developing and evaluating existing/new monolingual text in an image is that it is not linked and *** is no method to reuse the online news images because of the unavailability of standardized benchmark corpora,especially for South Asian *** corpus is a vital resource for developing and evaluating text in an image to reuse local news systems in general and specifically for the Urdu *** of indexing,primarily semantic indexing of the daily news items,makes news items impracticable for any ***,the most straightforward search facility does not support these unindexed news *** study addresses this gap by associating and marking the newspaper images with one of the widely spoken but under-resourced languages,i.e.,*** present work proposed a method to build a benchmark corpus of news in image form by introducing a web *** corpus is then semantically linked and annotated with daily news *** techniques are proposed for image annotation,free annotation and fixed cross examination *** second technique got higher *** news ontology in protégéusing OntologyWeb Language(OWL)language and indexed the annotations under *** application is also built and linked with protégéso that the readers and journalists have an interface to query the news items ***,news items linked together will provide complete coverage and bring together different opinions at a single location for readers to do the analysis themselves.

关键词： Annotations corpus information retrieval semantic ontology

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Data Mining Approach in Predicting House Price for Automated Property Appraiser Systems 6th

Data Mining Approach in Predicting House Price for Automated...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Yousir, Naeem Th. Abdulameer, Shaymaa Mohammed Mostafa, Salama A. College of Information Engineering Al-Nahrain University Baghdad10072 Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor86400 Malaysia

ISBN: (纸本)9789819930098

The house price prediction model can be a logistic tool in assisting individuals and companies to determine the cost of a property or a house on sale and the best time to acquire a house. With the ever-increasing in house price rates, data mining approaches have been used as a property appraiser system to predict a house price based on house features before making a deal. Existing research in house prediction involves collecting a number of house price estimation attributes into a dataset of house price features. This study is aimed at building a model for predicting house price based on the house price features dataset. Then dataset is run through machine learning (ML) algorithms to learn the pattern and predict new house price cases. The cross-industry standard process for data mining (CRISP-DM) method is used for this research. Decision tree regression (DTR), linear regression (LR), multiple linear regression (MLR), and random forest regression (RFR) are the ML algorithms applied in this research. The results have shown that RFR is the most suitable algorithm for this dataset, with an average coefficient of determination (R square) of 82.09%, followed by the DTR with an R square score of 73.32% when using different training and learning splitting ratios. An application of the RFR model can be used in real estate companies for estimating the prices of properties. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multiple linear regression

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CNNs in Crop Care: A Comparative Analysis of Tomato Disease Detection Models 2

CNNs in Crop Care: A Comparative Analysis of Tomato Disease ...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ali, Ali M Nashwan, Shadi Al-Qerem, Ahmad Aldweesh, Amjad Alauthman, Mohammad Elgamal, Zenab Almomani, Ammar AlAhliyya Amman University Faculty Of Engineering Communications And Computer Engineering Department Amman19328 Jordan Faculty Of Information Technology Cybersecurity Department Amman11831 Jordan Zarqa University Faculty Of Information Technology Computer Science Department Zarqa13110 Jordan College Of Computing And It Shaqra University Shaqra Saudi Arabia University Of Petra Faculty Of Information Technology Department Of Information Security Amman Jordan School Of Computing Skyline University College University City Of Sharjah Sharjah United Arab Emirates School Of Computing Skyline University College Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I.T. Department Irbid Jordan

ISBN: (纸本)9798350394962

Timely and accurate detection of plant diseases is critical for sustainable crop production. This paper presents a comparative analysis of convolutional neural network (CNN) models for automated tomato leaf disease detection, including VGG16, VGG19, AlexNet and Google Net. A dataset of tomato leaf images with multiple disease types was utilized to train and test the performance of each CNN architecture. Our results demonstrate that the VGG16 model achieved the highest accuracy of 99.38% in classifying tomato leaf diseases. Compared to AlexNet and VGG19 models which obtained 98.67% and 99.29% accuracy respectively, the superior performance of VGG16 highlights its suitability for deploying robust tomato disease detection systems to enable prompt disease control interventions. Our work provides valuable insights on harnessing deep learning for agricultural disease surveillance, allowing farmers and agronomists to identify crop infections early and implement timely management practices to reduce yield losses. © 2024 IEEE.

关键词： Convolutional neural networks

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COCP:Coupling Parameters Content Placement Strategy for In-Network Caching-Based Content-Centric Networking

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computers, Materials & Continua 2022年第6期71卷 5523-5543页

作者： Salman Rashid Shukor Abd Razak Fuad A.Ghaleb Faisal Saeed Eman H.Alkhammash School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia College of Computer Science and Engineering Taibah UniversityMedina 42353Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

On-path caching is the prominent module in Content-Centric Networking(CCN),equipped with the capability to handle the demands of future networks such as the Internet of Things(IoT)and vehicular *** main focus of the CCN caching module is data dissemination within the *** of the existing strategies of in-network caching in CCN store the content at the maximumnumber of routers along the downloading ***,content redundancy in the network increases significantly,whereas the cache hit ratio and network performance decrease due to the unnecessary utilization of limited cache ***,content redundancy adversely affects the cache resources,hit ratio,latency,bandwidth utilization,and server *** proposed an in-network caching placement strategy named Coupling Parameters to Optimize Content Placement(COCP)to address the content redundancy and associated *** novelty of the technique lies in its capability tominimize content redundancy by creating a balanced cache space along the routing path by considering request rate,distance,and available cache *** proposed approach minimizes the content redundancy and content dissemination within the network by using appropriate locations while increasing the cache hit ratio and network *** COCP is implemented in the simulator(Icarus)to evaluate its performance in terms of the cache hit ratio,path stretch,latency,and link *** experiments have been conducted to evaluate the proposed COCP *** proposed COCP technique has been compared with the existing state-of-theart techniques,namely,Leave Copy Everywhere(LCE),Leave Copy Down(LCD),ProbCache,Cache Less forMore(CL4M),and *** results obtained with different cache sizes and popularities show that our proposed caching strategy can achieve up to 91.46%more cache hits,19.71%reduced latency,35.43%improved path stretch and 38.14%decreased link *** results confirm that the proposed COCP strateg

关键词： Content-centric networking on-path caching content redundancy security privacy data dissemination internet of things

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An Efficient Internet Traffic Classification System Using Deep Learning for IoT

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computers, Materials & Continua 2022年第4期71卷 407-422页

作者： Muhammad Basit Umair Zeshan Iqbal Muhammad Bilal Jamel Nebhen Tarik Adnan Almohamad Raja Majid Mehmood Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan Division of Computer and Electronics Systems Engineering Hankuk University of Foreign StudiesYongin-siKorea Electrical-Electronics Engineering Department Faculty of EngineeringKarabük University78050KarabükTurkey Prince Sattam bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia Information and Communication Technology Department School of Electrical and Computer EngineeringXiamen University MalaysiaSepang43900Malaysia

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 *** IoT devices are connected to a network therefore prone to *** management tasks and network operations such as security,intrusion detection,Quality-of-Service provisioning,performance monitoring,resource provisioning,and traffic engineering require traffic *** 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 ***,machine learning-based models incline to misclassify internet traffic due to improper feature *** this research,an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet *** examine the performance of the proposed technique,Moore-dataset is used for training 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 *** 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.

关键词： Deep learning internet traffic classification network traffic management QoS aware application classification

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Integrating Structural information: Comparing Classification-Based and Segmentation-Based Predictors in Bioinformatics (case study: protein-peptide region residue-level interaction) using structural information 29

Integrating Structural Information: Comparing Classification...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Shafiee, Shima Fathi, Abdolhossein Safari, Faezeh Department of Computer Engineering and Information Technology Razi University Kermanshah Iran School of Electrical Eng. and Computer Science Faculty of Eng. Architecture and Info. Tech University of Queensland BrisbaneQLD Australia

ISBN: (纸本)9798331523114

Understanding the regions where proteins bind to peptides is vital for studying diseases like cancer, as it sheds light on various cellular processes and aids in drug discovery. Although researchers can experimentally investigate protein-peptide interactions at the region level by analyzing the structures of their complexes, only a limited number of these structures are known. Additionally, experiments to determine these interactions can be both costly and time-consuming. Computational methods provide an alternative and complementary approach to experimental techniques. As a result, using computational methods to predict peptide-binding regions can significantly enhance the efficiency and cost-effectiveness of experimental research. To leverage these advantages, this study introduces a new method named Structure-based Prediction of Residue-level Interaction of Peptides (SPRINT-Pep), designed to pinpoint the binding regions between proteins and peptides. This approach leverages two distinct predictive models: a classification-based model combining DeepResNet with a Support Vector Machine (SVM) and a segmentation-based model called DeepU-Net. Our predictions outperform other state-of-the-art methods in terms of F-measures (by at least 23%), precision (by at least 3.7%), and the balance between sensitivity and specificity (by about 7.7%). These findings demonstrate the proficiency of the proposed method. © 2025 IEEE.

关键词： Bioinformatics

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Clustering Approach for Modeling Course Difficulty Level in Adaptive Learning

Clustering Approach for Modeling Course Difficulty Level in ...

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2024 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2024

作者： Imamah Yuhana, Umi Laili Djunaidy, Arif Pardede, Eric Purnomo, Mauridhi Hery Departement of Electrical Engineering Faculty of Intelligent Electrical Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Informatics Faculty of Intelligent Electrical Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Information Systems Faculty of Intelligent Electrical Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Departement of Computer Science Information Technology La Trobe University BundooraVIC3086 Australia

ISBN: (纸本)9798350322996

The variations in students' abilities present challenges in achieving desired learning outcomes. Students exhibit different levels of comprehension regarding the complexity of learning materials. In this study, we aim to model the difficulty levels of learning materials using a clustering approach to offer personalized learning material recommendations within an adaptive learning framework. Data were collected from student scores of Data Structure course obtained between 2020 and 2022 at the University of Trunojoyo Madura. The dataset modeling process adheres to the CRISP-DM Methodology, employing the K-Means clustering method and Euclidean Distance. We employed the Within-Cluster Sum of Squares (WCSS) metric and silhouette score to identify the optimal K-value, which resulted in a value of 5, accompanied by a silhouette score of 0.629. Our analysis pinpointed Stack and Deque as the easiest, while Linked List emerged as the most challenging course material. Accordingly, we will assign a label of 1 to the easiest materials, 2 to those of medium difficulty, and 3 to the most challenging ones. These labels will serve as criteria for recommending suitable learning materials to students based on their knowledge level. We will assess the ratio between the overall knowledge level score and the difficulty level of the material. The ratio value will serve as a reference for determining material recommendations for students. Materials categorized with the highest difficulty levels are exclusively suggested to students demonstrating faster learning abilities with a high value of ratio, and vice versa. This approach is feasible for implementation in adaptive learning. © 2024 IEEE.

关键词： Contrastive Learning

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Mathematical Modelling of Quantum Kernel Method for Biomedical Data Analysis

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computers, Materials & Continua 2022年第6期71卷 5441-5457页

作者： Mahmoud Ragab Ehab Bahauden Ashary Maha Farouk S.Sabir Adel A.Bahaddad Romany F.Mansour Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Mathematics Department Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

This study presents a novelmethod to detect themedical application based on Quantum Computing(QC)and a few Machine Learning(ML)*** has a primary advantage i.e.,it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of ***,this model is suggested for medical application only by recent researchers.A novel strategy i.e.,Quantum KernelMethod(QKM)is proposed in this paper for data *** this QKM process,Linear Tunicate Swarm Algorithm(LTSA),the optimization technique is used to calculate the loss function initially and is aimed at medical *** output of optimization is either 0 or 1 i.e.,odd or even in *** this output value,the data is identified according to the ***,the method also reduces time,saves cost and improves the efficiency by feature selection process i.e.,Filter *** the features are extracted,QKM is deployed as a classification model,while the loss function is minimized by *** motivation of the minimal objective is to remain ***,some computations can be performed more efficiently by the proposed *** testing,the test data was evaluated by minimal loss *** outcomes were assessed in terms of accuracy,computational time,and so *** this,databases like Lymphography,Dermatology,and Arrhythmia were used.

关键词： Medical data classification feature selection qkm classifier ltsa optimization

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Internet of vehicle's resource management in 5G networks using AI technologies: Current status and trends

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IET COMMUNICATIONS 2022年第5期16卷 400-420页

作者： Elfatih, Nada M. Hasan, Mohammad Kamrul Kamal, Zeinab Gupta, Deepa Saeed, Rashid A. Ali, Elmustafa Sayed Hosain, Md. Sarwar Department of Electrical and Electronics Engineering Red Sea University Port Sudan Sudan Faculty of Information Science and Technology The National University of Malaysia (UKM) Bangi Selangor Malaysia Amity Institute of Information Technology Amity University Uttar Pradesh Delhi NCR India Department of computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif 21944 Saudi Arabia Department of Electronics Engineering Sudan University of Science and Technology Khartoum Sudan Department of Information and Communication Engineering Pabna University of Science and Technology Pabna 6600 Bangladesh

The Internet of Vehicles (IoV) and Vehicle-to-Everything (V2X) concept have emerged from IoT technology, which refers to connecting many vehicles with various applications to the internet. The 5G new radio is based on a cloud-radio access network (CRAN), considered as the communication infrastructure for IoV. However, due to the significant challenges and issues, researchers have been working on IoV and V2X. One of the main challenges for V2X is resource allocation and management for a high-speed vehicular environment. This paper discusses and provides complete detail for resource allocation and management for IoV over 5G RAN networks focusing on artificial intelligence techniques. The paper also presented reviews on integrating the multi-layers of vehicular network architecture with AI strategy to identify advancement and future directions for resource allocation and management issues.

关键词： Internet of Things Vehicle to Everything artificial intelligence techniques resource allocation IoT technology IoV radio access networks vehicle resource management V2X 5G mobile communication Internet of Vehicles communication infrastructure artificial intelligence vehicular ad hoc networks 5G RAN networks 5G new radio high-speed vehicular environment 5G networks resource management telecommunication computing

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Enhancing real-time video surveillance: a circular queue-guided stacked parallel convolution network for efficient suspicious activity detection

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Earth Science Informatics 2025年第3期18卷 1-27页

作者： Sekar, S. Narayanan, S. Sirenjeevi, P. Sethuraman, Ravikumar Department of Information Technology SRM Valliammai Engineering College Chennai India Department of Information Technology St. Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN Deemed-to-Be University Bengaluru India

Suspicious activity detection is crucial for public safety, enabling the prevention of crimes such as theft, robbery, and harassment. The rapid advancement of edge computing has enabled real-time data processing at the source, reducing latency and bandwidth constraints. The core idea of this work is to develop a Smart Surveillance System that combines Edge-Cloud-IoT architecture for automated suspicious activity recognition. Therefore, this paper introduces a novel deep learning model named Circular Queue-Guided Attentive Stacked Parallel Convolution Network to enhance suspicious activity recognition in surveillance videos. The Stacked Parallel Convolution module employs a variety of kernel sizes to extract multi-scale features, while the Co-Attention Mechanism improves feature representation. A Circular Queue is used to reduce inter-class similarity problems in convolutional neural networks and increase classification accuracy by utilizing temporal information. The decision-making procedure improves recognition reliability by depending on the classification labels that appear most frequently during a given time span. Real-world surveillance footage is used for experimental evaluations, which are processed on a Raspberry Pi edge computing architecture with cloud backup provided by Amazon Web Services. The experimental results provided that the proposed model achieved an accuracy of 99.18%, outperforming existing approaches across metrics such as recall, precision, and F1-score. The proposed model offers a practical solution for real-time monitoring, with reduced response time and power consumption compared to traditional methods. The result demonstrates the model’s potential for real-time automated surveillance applications.

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