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2023 Intelligent Computing and Control for Engineering and business systems, ICCEBS 2023

作者： Sathya, T. Ajai, K. Suretha, S. Dhamotharakkannan, M. Vidhusha, M. Sri Sairam Engineering College Computer Science & Business Systems Chennai India

ISBN: (纸本)9798350394580

Augmented Reality (AR) is an emerging technological advancement that facilitates immersive and interactive experiences within the context of the real-world environment. The main goal of this project is to lead the way in creating a cutting-edge educational platform based on 3D Augmented Reality. This platform is designed to replace long-standing conventional two-dimensional (2D) educational approaches. The limitations of 2D education in terms of lacking immersive qualities and interactivity have rendered them increasingly ineffective in engaging students. The proposed remedy involves the creation of comprehensive 3D educational models and components, integrated with AR technology. This AR application presents a considerably more appealing and engaging alternative for students to elucidate abstract concepts. This research paper is meticulously designed to explore the applications of AR within the sphere of education, with a particular focus on its relevance in clarifying fundamental engineering concepts, including but not limited to Finite Automata, Data Structures, Operating systems, and Database Management systems. © 2023 IEEE.

关键词： Application Augmented Reality Education fundamental engineering concepts interactive

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Cardiovascular Disease Prediction Using Randelistic Algorithm

Cardiovascular Disease Prediction Using Randelistic Algorith...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Ananthi, M. Narayanan, Aathrayee Shankara Dhiraj Prasad, T.P. Jai Vignesh, R. Sri Sairam Engineering College Computer Science & Business Systems Chennai India

ISBN: (纸本)9798350333244

Diagnosis of heart diseases pose a major problem due to human error and faulty interpretation of data. Existing medical tools are costly and has a higher percentage of true negatives, and do not fare well in the areas of prediction., putting people at danger due to carelessness. Timely prediction plays an important role in saving patient's lives. Prediction models like these which use Machine Learning can be used in Hospitals and as well as for personal use to reduce the number of cases of True Negative due to diagnostic negligence and to keep track of various symptoms over a period of time that acts as a detailed history to improve accuracy of the learning model designed using various mathematically intensive algorithms. The solution proposed is to use multiple algorithms namely Logistic Regression[1], Random Forest Algorithm[1] and Decision trees[1] in the Boosting process, so as to reduce the number true negatives and false positives and get the best result during the majority voting. © 2023 IEEE.

关键词： Decision trees

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ABES: attention bi-directional ensemble SVM for early detection of brain tumors

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Neural Computing and Applications 2024年第26期36卷 16179-16193页

作者： Subramaniam, Erana Veerappa Dinesh Krishnasamy, Valarmathi Department of Computer Science and Business Systems Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering P.S.R. Engineering College Sivakasi India

Brain tumor is the most serious and deadly disease, and it is formed due to abnormal cell production. There are two different sorts of tumors including benign (non-cancerous) and malignant (cancerous), and the third level of a brain tumor is cancerous, which is a highly deadly form of cancer. Detecting brain tumors early is crucial for accurate diagnosis and efficient treatment planning. Although numerous techniques are established, the prediction techniques are time-consuming, have higher computational complexity, prone to overfitting, and have limited accuracy. Thus we proposed an Attention Bi-directional Gated Recurrent Unit Ensemble Support Vector Machine (ABES model) for detecting brain tumors. Here, the Bi-GRU layer learns the most important context information for every image. The attention layer also uses the attention mechanism to assign weights to each Bi-GRU layer output. The ensemble SVM classifier performs the categorization of brain tumors. Then, the training set is used to train the Attention BiGRU model and Ensemble SVM classifiers. The ABES classifier weights are optimized by utilizing the improved whale optimization algorithm. To demonstrate the efficiency of our proposed method, we compare it to four existing methods including Fully Automatic Heterogeneous Segmentation-based Support Vector Machine, Whale Harris Hawks Optimization-based Deep Learning, Machine learning-based back propagation neural networks, and Deep Convolutional Neural Network. Also, the ABES model is validated using the MRI dataset and the Figshare brain tumor dataset. Accuracy, precision, specificity, and sensitivity are the performance metrics used to evaluate classification performance, which achieves 97.2% accuracy, 98.61% Precision, 96.48% Specificity, and 97.34% Sensitivity, respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Support vector machines

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A Convolutional Neural Network Model for Wheat Crop Disease Prediction

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computers, Materials & Continua 2023年第5期75卷 3867-3882页

作者： Mahmood Ashraf Mohammad Abrar Nauman Qadeer Abdulrahman A.Alshdadi Thabit Sabbah Muhammad Attique Khan Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21577Saudi Arabia Department of Computer Science Bacha Khan University CharsaddaKhyber Pakhtunkhwa24461Pakistan Department of Computer Science Federal Urdu University of ArtsScience&TechnologyIslamabad45570Pakistan Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddah21577Saudi Arabia Faculty of Technology and Applied Sciences Al-Quds Open UniversityAl-Bireh1804Palestine Department of Computer Science HITEC UniversityTaxila47080Pakistan

Wheat is the most important cereal crop,and its low production incurs import pressure on the *** fulfills a significant portion of the daily energy requirements of the human *** wheat disease is one of the major factors that result in low production and negatively affects the national ***,timely detection of wheat diseases is necessary for improving *** CNN-based architectures showed tremendous achievement in the image-based classification and prediction of crop ***,these models are computationally expensive and need a large amount of training *** this research,a light weighted modified CNN architecture is proposed that uses eight layers particularly,three convolutional layers,three SoftMax layers,and two flattened layers,to detect wheat diseases *** high-resolution images were collected from the fields in Azad Kashmir(Pakistan)and manually annotated by three human *** convolutional layers use 16,32,and 64 *** filter uses a 3×3 kernel *** strides for all convolutional layers are set to *** this research,three different variants of datasets are *** variants S1-70%:15%:15%,S2-75%:15%:10%,and S3-80%:10%:10%(train:validation:test)are used to evaluate the performance of the proposed *** extensive experiments revealed that the S3 performed better than S1 and S2 datasets with 93%*** experiment also concludes that a more extensive training set with high-resolution images can detect wheat diseases more accurately.

关键词： Machine learning wheat crop disease prediction convolutional neural network artificial intelligence

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Coati Optimization-Based Energy Efficient Routing Protocol for Unmanned Aerial Vehicle Communication

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computers, Materials & Continua 2023年第6期75卷 4805-4820页

作者： Hanan Abdullah Mengash Hamed Alqahtani Mohammed Maray Mohamed K.Nour Radwa Marzouk Mohammed Abdullah Al-Hagery Heba Mohsen Mesfer Al Duhayyim Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial Intelligence and Unit of CybersecurityKing Khalid UniversityAbha62529Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Sciences College of Computing and Information SystemsUmm Al-Qura UniversityMecca24382Saudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydah52571Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile *** difficulty stems from features other than mobile ad-hoc network(MANET),namely aerial mobility in three-dimensional space and often changing *** the UAV network,a single node serves as a forwarding,transmitting,and receiving node at the same ***,the communication path is multi-hop,and routing significantly affects the network’s performance.A lot of effort should be invested in performance analysis for selecting the optimum routing *** this motivation,this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication(COAER-UAVC)*** presented COAER-UAVC technique establishes effective routes for communication between the *** is primarily based on the coati characteristics in nature:if attacking and hunting iguanas and escaping from ***,the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay.A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC *** experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.

关键词： Artificial intelligence unmanned aerial vehicle data communication routing protocol energy efficiency

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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The Impact of Trendy Topics on Information Diffusion 12

The Impact of Trendy Topics on Information Diffusion

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12th International Conference on Software and Information Engineering, ICSIE 2023

作者： Fahmy, Sara G Khalil, Rana Moukhtar, Hebat Allah Adel Elzanfaly, Doaa S Abdelgaber, Khaled M Department of Information Systems Faculty of Informatics and Computer Science The British University in Egypt Egypt Faculty of Informatics and Computer Science The British University in Egypt Egypt

ISBN: (纸本)9798400708107

关键词： Information Diffusion SIR model SI_{t_w}R model Social Networks Structure of Network Textual Content Topic Identification Trend Extraction Trendy Topics

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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Enhanced Prediction of Lung Cancer Stages using SVM and Medical Imaging 3

Enhanced Prediction of Lung Cancer Stages using SVM and Medi...

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3rd International Conference on Electronics and Renewable systems, ICEARS 2025

作者： Manimaran, P. Vignesh, R. Vignesh, B. Thilak, G. K.S. Rangasamy College of Technology Department of Computer Science And Business Systems Tiruchencode TamilNadu Namakkal India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

This study presents a comprehensive advance that be SVM, KNN, and DS method do get for lung cancer segmentation in aesculapian images. Lineament vectors created from excerpt feature of speech and aim on a metameric dataset were the world-class exercise of the SVM technique. Data Point structure and KNN algorithms are and then utilize to classify the examination set 's region, and post - processing techniques are applied to enhance segmentation. Among the evaluation standard employ to gauge the efficacy of the fuse approach path are accuracy, recall, F1 sexual conquest, and dice coefficient. Combining the SVM, DS, and KNN algorithm creates a flexible framework for lung cancer segmentation that importantly ameliorate electronic computer - aided diagnosis and medical picture analysis. © 2025 IEEE.

关键词： Lung cancer

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Liver Disease Prediction Using Novel Ensemble Techniques with Catboost and LightGBM

Liver Disease Prediction Using Novel Ensemble Techniques wit...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Udhaya, T. Moulitharan, M. Arockia Jegan, S. Akash, M. K.S. Rangasamy College of Technology Department of Computer Science and Business Systems Tamil Nadu Namakkal India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Using Stream lit, this project proposes a web application to predict liver disease based on patient demographics and a number of biochemical markers. The developed program makes use of a CatBoost classifier that was trained on an extensive dataset that include characteristics like age, sex, and particular blood test results. The application allows users to enter their data, processes it, and then uses the learnt model to forecast the risk of liver disorders. Users can easily grasp their liver health condition because to the results' clear and easy-to-understand style. By improving early diagnosis and knowledge of liver illnesses, this technology hopes to improve health outcomes by facilitating prompt intervention. After they submit their data, the model analyses the inputs and makes predictions about the probability of different liver disorders. © 2025 IEEE.

关键词： Prediction models

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