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9th International Conference on Computing and Informatics, ICOCI 2023

作者： Ahmad, Farzana Kabir Kamaruddin, Siti Sakira ali, Adnan Hussein Ibrahim, Farah Lia School of Computing College of Arts and Sciences Universiti Utara Malaysia Sintok Kedah06000 Malaysia Department of Computer Science College of Computer Science and Engineering Al-Ahgaff University Hadhramaut Yemen

ISBN: (纸本)9789819995882

The social networks and news ecosystem provide valuable social information, however, the rise of deceptive content such as fake news generated by social media users, poses an increasing threat to the propagation and diffusion of fake news over the social network and among users. Low-quality news and misinformation spread on social media had negative impacts on individuals and society. Hence, it is essential to detect fake news to ensure the spread of accurate and truthful information. To address this problem, a new approach using Binary Bat algorithm (BBA) for fake news detection (FND) on Twitter data is proposed in this paper. Twitter data usually generates massive feature space which might consist of irrelevant features that could jeopardize the subsequent process. The proposed FND approach involves four stages, namely data collection, pre-processing, feature extraction, and fake news detection. The proposed techniques are tested on PHEME dataset, and the experimental results are measured in term average of Precision (PR), Recall (R), F-measure (F), and Accuracy (ACC). The experimental results show that the BBA algorithm has outperformed the Social Spider Optimization (SSO) algorithm. Thus, BBA is a promising solution for solving high-dimensionality feature space in fake news Twitter data. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： K-means clustering

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Natural Language Processing with Optimal Deep Learning Based Fake News Classification

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computers, Materials & Continua 2022年第11期73卷 3529-3544页

作者： Sara Aalthubiti Fayadh alenezi Romany F.Mansour Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

The recent advancements made in World Wide Web and social networking have eased the spread of fake news among people at a faster *** most of the times,the intention of fake news is to misinform the people and make manipulated societal *** spread of low-quality news in social networking sites has a negative influence upon people as well as the *** order to overcome the ever-increasing dissemination of fake news,automated detection models are developed using Artificial Intelligence(AI)and Machine Learning(ML)*** latest advancements in Deep Learning(DL)models and complex Natural Language Processing(NLP)tasks make the former,a significant solution to achieve Fake News Detection(FND).In this background,the current study focuses on design and development of Natural Language Processing with Sea Turtle Foraging Optimizationbased Deep Learning Technique for Fake News Detection and Classification(STODL-FNDC)*** aim of the proposed STODL-FNDC model is to discriminate fake news from legitimate news in an effectual *** the proposed STODL-FNDC model,the input data primarily undergoes pre-processing and Glove-based word ***,STODL-FNDC model employs Deep Belief Network(DBN)approach for detection as well as classification of fake ***,STO algorithm is utilized after adjusting the hyperparameters involved in DBN model,in an optimal *** novelty of the study lies in the design of STO algorithm with DBN model for *** order to improve the detection performance of STODL-FNDC technique,a series of simulations was carried out on benchmark *** experimental outcomes established the better performance of STODL-FNDC approach over other methods with a maximum accuracy of 95.50%.

关键词： Natural language processing text mining fake news detection deep belief network machine learning evolutionary algorithm

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Concrete Structure Defect Detection using You Only Look Once Version 5 with alexNet 2

Concrete Structure Defect Detection using You Only Look Once...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Devi, T. ali, Hassan Mohamed alsalami, Zaid Kumar, S. Senthil Sangeetha, K. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam India

ISBN: (纸本)9798331504960

Concrete structures are made from cement, water, and admixtures, which are used in buildings, water management systems, and foundations. There are many face pressing issues such as corrosion, cracking, and chemical attacks, requiring innovative solutions for sustainable infrastructure. Various methods have been proposed previously to recognize the defects of concrete structures. To overcome these problems, a Deep Learning (DL) model of You Only Look Once version 5 with alex-Krizhevsky Network (YOLOv5-alexNet) is proposed for detecting the defects of concrete structures. Primarily, a Surface Crack Detection (SCD) dataset is considered as an input for recognizing the defects of concrete structures. For preprocessing, image resizing and normalization, data augmentation, contrast enhancement, image filtering, and image binarization are considered for efficient processing, enhancing robustness, improving crack visibility, edge detection, and to simplify crack detection. Then for feature extraction, U-shaped Network (U-Net) is employed to reduce noise sensitivity, and to fine-tune the model. A YOLOv5-alexNet is considered for classification to improve performance and generalizability, and random search for hyperparameter tuning. The proposed YOLOv5-alexNet model attained higher performance metrics such as accuracy, precision, recall, and f1 score of 99.57%, 98.23%, 98.65%, and 99.86%, when compared to other existing methods like Convolutional Neural Network (CNN). © 2024 IEEE.

关键词： Concrete buildings

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Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

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computers, Materials & Continua 2022年第7期72卷 141-156页

作者： R.Gopi Mahantesh Mathapati B.Prasad Sultan Ahmad Fahd N.al-Wesabi Manal Abdullah alohali Anwer Mustafa Hilal Department of Computer Science and Engineering Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India Department of Computer Science and Engineering RajaRajeswari College of EngineeringBengaluru560074India Department of Information Technology Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Computer Science College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia&Faculty of Computer and ITSana’a UniversitySana’a15347Yemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

VehicularAd hoc Network(VANET)has become an integral part of Intelligent Transportation Systems(ITS)in today’s *** is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real *** is susceptible to security issues,particularly DoS attacks,owing to maximum unpredictability in ***,effective identification and the classification of attacks have become the major requirements for secure data transmission in *** the same time,congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections,for a *** this motivation,the current research paper presents an intelligent DoS attack detection with Congestion Control(IDoS-CC)technique for *** presented IDoSCC technique involves two-stage processes namely,Teaching and Learning Based Optimization(TLBO)-based Congestion Control(TLBO-CC)and Gated Recurrent Unit(GRU)-based DoS detection(GRU-DoSD).The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the *** algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel *** is applied to avoid congestion on ***,GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the *** outcomes from a series of simulation analyses highlight the supremacy of the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network.

关键词： VANET intelligent transportation systems congestion control attack detection dos attack deep learning

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Enhancing Data Aggregation in Cluster-Based Wireless Sensor Networks with LCSS: Longest Common Subsequence Empowerment 8th

Enhancing Data Aggregation in Cluster-Based Wireless Sensor ...

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8th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2024

作者： Jamil, Muhammad Farooq, Umer Tariq, Noshina alwakid, Ghadah Humayun, Mamoona Khan, Nadia Department of Computer Science Abdul Wali Khan University Mardan Pakistan Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Department of Avionics Engineering Air University Islamabad Pakistan Department of Computer Science College of Computer and Information Sciences Jouf University Sakaka Al-Jouf Saudi Arabia Department of Information Systems College of Computer and Information Sciences Jouf University Sakaka Al-Jouf Saudi Arabia Institute of Computer Sciences and Information Technology Faculty of Management and Computer Sciences The University of Agriculture Peshawar Pakistan

ISBN: (纸本)9789819793266

A wireless sensor network (WSN) is a system of interconnected sensors that can gather environmental information. On the other hand, data redundancy is a common source of problems with WSNs. The literature presents a plethora of approaches. Despite these mechanisms’ best efforts, most current systems still need help with difficulties like noise, redundancy, or application specificity. Thus, we proposed an algorithm to overcome all the problems with redundant and highly correlated data. The longest common subsequence (LCSS)-enabled sliding window algorithm can resolve issues in constrained networks related to energy conservation and congestion control. The proposed method exhibits remarkable performance on multiple benchmark datasets. According to the result analysis, the suggested technique can outperform its equivalents for a range of data sets by about 32%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Congestion control (communication)

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TMRE:Novel algorithm for Computing Daily Reference Evapotranspiration Using Transformer-Based Models

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computers, Materials & Continua 2025年第5期83卷 2851-2864页

作者： Bushra Tayyaba Muhammad Usman Ghani Khan Talha Waheed Shaha al-Otaibi Tanzila Saba National Center of Artificial Intelligence(NCAI) Al Khwarizmi Institute of Computer Sciences(KICS)Lahore54890Pakistan Department of Computer Science University of Engineering and Technology LahoreLahore54890Pakistan Artificial Intelligence&Data Analytics Lab Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.

关键词： Reference evapotranspiration ETo transformer informer autoformer FEDformer timeseries forecasting self-attention

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Identifying and Categorizing Skin Disorders by Using CNN to Diagnose Five Prevalent Skin Disease from Skin Images 8th

Identifying and Categorizing Skin Disorders by Using CNN to ...

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Aditya, T. Udaya Laxmi Racharla, Suneetha Bhavani, A. Durga Ramamohan, P. Pattabhi Lydia, E. Laxmi althubiti, Sara A. Department of AIML Affiliated to JNTUK Aditya Engineering College AP Surampalem India Department of CSE Affiliated to JNTUK Sasi Institute of Technology and Engineering AP Tadepalligudem India Department of IT Affiliated to JNTUK Aditya Engineering College AP Surampalem India Vijayawada India Department of Computer Science College of Computer and Information Sciences Majmaah University Al-Majmaah11952 Saudi Arabia

ISBN: (纸本)9789819784219

The most common illnesses worldwide are those related to the skin. Despite being widespread, therapy is exceedingly challenging and necessitates substantial industry knowledge. Most skin conditions are brought on by bacterial, viral, allergic, or fungal infections. For the rapid and precise treatment of skin problems, medical technology based on photonics and the development of lasers is applied. However, there are few and very expensive medical devices available for this type of diagnostic. Deep learning algorithms are useful in these circumstances. Early skin disease detection is crucial for effective therapy. Convolution Neural Network (CNN), a deep learning approach, may aid in identifying the issue early on. We employed a combination of computer vision and deep learning to precisely diagnose illnesses. The frequency of frequent human supervision has decreased as a result of the application of Deep Learning algorithms. For the classification of skin diseases, a dataset of 5,633 photographs that are classified into 5 groups has been collected. They consist of urticaria (hives), acne, eczema, melanoma, and psoriasis. The model also lists the recommended treatments for the skin condition as well as the precautions that must be taken. 83% accuracy in classifying skin diseases is achieved using the CNN algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Oncology

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A novel category detection of social media reviews in the restaurant industry

A novel category detection of social media reviews in the re...

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作者： Khan, Mohib Ullah Javed, Abdul Rehman Ihsan, Mansoor Tariq, Usman National University of Computer and Emerging Sciences Islamabad Pakistan Department of Cyber Security Air University Islamabad Pakistan The University of Salford Manchester United Kingdom College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabia

Social media platforms have enabled users to share their thoughts, ideas, and opinions on different subject matters and meanwhile generate lots of information which can be adopted to understand people’s emotion towards certain products. This information can be effectively applied for Aspect Category Detection (ACD). Similarly, people’s emotions and recommendation-based Artificial Intelligence (AI)-powered systems are in trend to assist vendors and other customers to improve their standards. These systems have applications in all sorts of business available on multiple platforms. However, the current conventional approaches fail in providing promising results. Thus, in this paper, we propose novel convolutional attention-based bidirectional modified LSTM by combining the techniques of the next word, next sequence, and pattern prediction with ACD. The proposed approach extracts significant features from public reviews to detect entity and attribute pair, which are treated as a sequence or pattern from a given opinion. Next, we trained our word vectors with the proposed model to strengthen the ACD process. Empirically, we compare the approach with the state-of-the-art ACD models that use SemEval-2015, SemEval-2016, and SentiHood datasets. Results show that the proposed approach effectively achieves 78.96% F1-Score on SemEval-2015, 79.10% F1-Score on SemEval-2016, and 79.03% F1-Score on SentiHood which is higher than the existing approaches. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Sentiment analysis

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CNN Based Multi-Object Segmentation and Feature Fusion for Scene Recognition

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computers, Materials & Continua 2022年第12期73卷 4657-4675页

作者： Adnan Ahmed Rafique Yazeed Yasin Ghadi Suliman Aalsuhibany Samia allaoua Chelloug Ahmad Jalal Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Latest advancements in vision technology offer an evident impact on multi-object recognition and scene *** sceneunderstanding task is a demanding part of several technologies,like augmented reality-based scene integration,robotic navigation,autonomous driving,and tourist *** visual information in contextually unified segments,convolution neural networks-based approaches will significantly mitigate the clutter,which is usual in classical frameworks during scene *** this paper,we propose a convolutional neural network(CNN)based segmentation method for the recognition of multiple objects in an ***,after acquisition and preprocessing,the image is segmented by using ***,CNN features are extracted from these segmented objects,and discrete cosine transform(DCT)and discrete wavelet transform(DWT)features are *** the extraction of CNN features and computation of classical machine learning features,fusion is performed using a fusion ***,to select theminimal set of features,genetic algorithm-based feature selection is *** order to recognize and understand the multi-objects in the scene,a neuro-fuzzy approach is *** objects in the scene are recognized,the relationship between these objects is examined by employing the object-to-object relation ***,a decision tree is incorporated to assign the relevant labels to the scenes based on recognized objects in the *** experimental results over complex scene datasets including SUN Red Green Blue-Depth(RGB-D)and Cityscapes’demonstrated a remarkable performance.

关键词： Convolutional neural network decision tree feature fusion neurofuzzy system

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Tidal-Tree-Mem: Toward Read-Intensive Key-Value Stores With Tidal Structure Based on LSM-Tree

Tidal-Tree-Mem: Toward Read-Intensive Key-Value Stores With ...

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作者： Ma, Chenlin Yang, Hao Wu, Shangyu Wang, Yi Mao, Rui Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China City University of Hong Kong Department of Computer Science Hong Kong Shenzhen University College of Computer Science and Software Engineering The Shenzhen Institute of Computing Sciences Shenzhen518060 China

The log-structured merge-tree (LSM-tree)-based key-value store has been widely adopted by many large-scale data storage applications for its excellent write performance. However, such write performance gains mainly come from scarifying read performance due to the leveled and log-structured intrinsic characteristics of the LSM-tree. Therefore, the critical challenge of the existing LSM-tree is how to improve the read efficiency by reducing read amplification. This article for the first time proposes Tidal-tree-Mem, a novel data structure where data flow inside the LSM-tree-like Tidal waves. First, a floating strategy is proposed to allow frequently accessed files at the bottom of the LSM-tree to move to higher positions, reducing read amplification. Second, a stretching strategy is proposed to vary the shape of the LSM-tree to adapt to workloads with different characteristics. To evaluate the performance of Tidal-tree-Mem, we conduct a series of experiments using standard benchmarks from YCSB. The experimental results show that Tidal-tree-Mem can effectively reduce read amplification and the overall latency by over 71.94% and 47.34%, respectively, compared with representative schemes. © 1982-2012 IEEE.

关键词： Benchmarking

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