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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ajitha, D. Dutta, Disha Saha, Falguni Giri, Parus Kant, Rohan School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Software Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Database Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Information Security Vellore India

ISBN: (纸本)9798350306613

Lip Reading AI is a discipline that is rapidly changing and has numerous applications in security, accessibility and human-computer interaction. This paper proposes a model which combines Convolutional Neural Networks (CNNs) to capture spatial capabilities, Long Short-Term Memory (LSTM) networks to examine temporal dependencies, and an adaptive interest mechanism. Meticulous preprocessing of the MIRACL VC-l dataset addressing challenges including one of a kind lip moves and occlusions accompanied with the aid of transitioning this study effortlessly to LRS2 dataset to complement lexemic versatility is one of its key function. The effects verify its robustness throughout unique datasets with superior overall performance towards cutting-edge techniques. Ablation checks suggest the crucial significance of every element in phrases of improving lip analyzing accuracy. Our proposed model version additionally suggests flexibility in restricted and naturalistic language situations. © 2024 IEEE.

关键词： Attention Mechanism Convolutional Neural Networks (CNNs) Deep Learning Lip Reading Long Short-Term Memory (LSTM) LRS2 Dataset MIRACLVC-J Dataset

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Automatic Detection of Weapons in Surveillance Cameras Using Efficient-Net

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computers, Materials & Continua 2022年第9期72卷 4615-4630页

作者： Erssa Arif Syed Khuram Shahzad Muhammad Waseem Iqbal Muhammad Arfan Jaffar Abdullah S.Alshahrani Ahmed Alghamdi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Informatics&Systems University of Management&TechnologyLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Faculty of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Department of Computer Science&Artificial Intelligence College of Computer Science&EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia

The conventional Close circuit television(CCTV)cameras-based surveillance and control systems require human resource *** all the criminal activities take place using weapons mostly a handheld gun,revolver,pistol,swords ***,automatic weapons detection is a vital requirement now a *** current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–*** time datasets,from local surveillance department’s test sessions are used for model training and *** consist of local environment images and videos from different type and resolution cameras that minimize the *** research also contributes in the making of Efficient-Net that is experimented and results in a positive *** results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research ***-Net algorithm gives better results than existing *** using Efficient-Net algorithms the accuracy achieved 98.12%when epochs increase as compared to other algorithms.

关键词： Detection algorithms machine learning machine vision video surveillance

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Deepfakes Catcher: A Novel Fused Truncated DenseNet Model for Deepfakes Detection 16th

Deepfakes Catcher: A Novel Fused Truncated DenseNet Model fo...

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16th International Conference on Information Technology and Applications, ICITA 2022

作者： Khalid, Fatima Javed, Ali Irtaza, Aun Malik, Khalid Mahmood Department of Computer Science University of Engineering and Technology Taxila Pakistan Department of Software Engineering University of Engineering and Technology Taxila Pakistan Department of Computer Science and Engineering Oakland University RochesterMI United States

ISBN: (纸本)9789811993305

In recent years, we have witnessed a tremendous evolution in generative adversarial networks resulting in the creation of much realistic fake multimedia content termed deepfakes. The deepfakes are created by superimposing one person’s real facial features, expressions, or lip movements onto another one. Apart from the benefits of deepfakes, it has been largely misused to propagate disinformation about influential persons like celebrities, politicians, etc. Since the deepfakes are created using different generative algorithms and involve much realism, thus it is a challenging task to detect them. Existing deepfakes detection methods have shown lower performance on forged videos that are generated using different algorithms, as well as videos that are of low resolution, compressed, or computationally more complex. To counter these issues, we propose a novel fused truncated DenseNet121 model for deepfakes videos detection. We employ transfer learning to reduce the resources and improve effectiveness, truncation to reduce the parameters and model size, and feature fusion to strengthen the representation by capturing more distinct traits of the input video. Our fused truncated DenseNet model lowers the DenseNet121 parameters count from 8.5 to 0.5 million. This makes our model more effective and lightweight that can be deployed in portable devices for real-time deepfakes detection. Our proposed model can reliably detect various types of deepfakes as well as deepfakes of different generative methods. We evaluated our model on two diverse datasets: a large-scale FaceForensics (FF)++ dataset and the World Leaders (WL) dataset. Our model achieves a remarkable accuracy of 99.03% on the WL dataset and 87.76% on the FF++ which shows the effectiveness of our method for deepfakes detection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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Predicting the Risks of Brain Stroke Using Machine Learning Models and Artificial Neural Network

Predicting the Risks of Brain Stroke Using Machine Learning ...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Nahid, Nafiz Hossain, Mehrab Rifat, Ariful Islam Khan Raisa, Meher Durdana Islam, Ashraful Sylhet Engineering College Department of Computer Science and Engineering Sylhet Bangladesh American International University Department of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Department of Software Engineering Dhaka Bangladesh University of Regina Department of Computer Science Regina Canada

ISBN: (纸本)9798350303452

The primary cause of death and disability worldwide is stroke. Accurate prediction models and identification of stroke risk factors can aid in early intervention and preventive measures. In this study, an approach based on machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), AdaBoost (AdB), Gradient Boosting (GB), Decision Tree (DT), and a unique Artificial Neural Network (ANN) model, is proposed to predict stroke risk at four different levels. To address the issue of imbalanced dataset, Synthetic Minority Over-sampling Technique coupled with Edited Nearest Neighbors (SMOTEENN) was used. 11 features were selected for training the models out of the initial 12 features, and the data-set was divided into an 80-20 train-test ratio for model assessment. The Decision Tree (DT) model achieved an accuracy of 98.53%. Additionally, a 1000 epoch Artificial Neural Network (ANN) model with various batch sizes was created;128 produced the best results. Stochastic Gradient Descent (SGD), Adam, and RMSprop were examined along with other optimizers, however SGD produced the highest accuracy rate of 99.21%. The results suggest that utilizing the selected features and the balanced data-set, machine learning algorithms such as the unique CNN model can accurately predict stroke risk at various levels. These models have the potential to aid in early detection of stroke risk and guide preventive interventions, thus improving patient care and outcomes. Further testing and implementation of these models in real clinical settings are warranted. © 2023 IEEE.

关键词： Decision trees

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Mathematical Methods for Reducing the Search Space for Solutions in 'Big Data' Analysis and Management 14

Mathematical Methods for Reducing the Search Space for Solut...

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14th International Conference on Advanced computer Information Technologies, ACIT 2024

作者： Syrotkina, Olena Kobti, Ziad Aleksieiev, Mykhailo Moroz, Dmytro Udovyk, Iryna Martynenko, Andrii University of Windsor School of Computer Science Windsor Canada Dnipro University of Technology Department of Software Engineering Dnipro Ukraine

ISBN: (纸本)9798350350036

This paper addresses mathematical methods to reduce or segment the search space for big data solutions into distinct subspaces with partial solutions. It is achieved by using a data organization structure of 'm-tuples based on ordered sets of arbitrary cardinality.' We provide a mathematical description of this structure and review contemporary methodologies to address similar problems. The study highlights how these properties can significantly reduce the analyzed state space and includes graphs that demonstrate changes in data volume within these subspaces. Additionally, we analyze how variations in four specific arguments affect data volume changes and provide an overview of the efficiency gains in resource use from applying the mathematical methods discussed. © 2024 IEEE.

关键词： Data structures

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Air Pollutant (CO) Forecasting: A Hybrid Approach Using Gooseneck Barnacle Optimization and Artificial Neural Networks 4

Air Pollutant (CO) Forecasting: A Hybrid Approach Using Goos...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Ahmed, Marzia Mohamad, Ahmad Johari Hassan, Md Maruf Bhuiyan, Touhid Department of Software Engineering Dhaka Bangladesh Faculty of Electrical & Electronics Engineering Technology Malaysia Southeast University Department of Computer Science and Engineering Dhaka Bangladesh School of IT VA United States

ISBN: (纸本)9798331518882

Air quality forecasting is critical for environmental monitoring and public health, and in this study, we propose a hybrid approach utilizing Gooseneck Barnacle Optimization (GBO) and Artificial Neural Networks (ANN) to enhance predictive accuracy. The air quality dataset, comprising pollutant concentrations (CO, NOx, benzene) and meteorological factors (temperature, humidity), is sourced from the UCI Machine Learning Repository. GBO is employed to fine-tune the ANN's hyperparameters (especially the weight and bias), as well as the number of layers, neurons, and activation functions, to improve regression-based forecasting. The GBOANN model achieves a Mean Squared Error (MSE) of 0.562 and a Root Mean Squared Percentage Error (RMSPE) of 5.63%, outperforming other metaheuristic methods like BMO, SSA, DA, MVO, and EMA. This study demonstrates the GBO-ANN model's potential for accurate real-time air quality forecasting, offering a robust, sustainable solution. © 2025 IEEE.

关键词： Air Quality GBO Metaheuristic Neural Network Time series forecasting

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Overnight Charging Mechanism of Battery Electric Buses for a Transit Network in Smart Cities 5

Overnight Charging Mechanism of Battery Electric Buses for a...

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5th Technology Innovation Management and engineering science International Conference, TIMES-iCON 2024

作者： Sobhani, Ehsan Yassine, Abdulsalam Ameli, Amir Riahinia, Shahin Department of Computer Science Lakehead University Thunder BayON Canada Department of Software Engineering Lakehead University Thunder BayON Canada Department of Electrical and Computer Engineering Lakehead University Thunder BayON Canada

ISBN: (纸本)9798350362602

—Battery electric buses (BEBs) are known for being eco-friendly transportation in smart cities. They are cost-effective compared to their diesel counterpart if BEBs are charged efficiently. There are two main charging approaches for BEBs, namely, off-service (overnight) and on-route (opportunity) charging. The overnight electricity rate is typically lower than the daytime rate. Consequently, overnight charging is considered the most affordable approach to meet the daily energy needs of the electrified transit network. This paper introduces a new intelligent overnight charging mechanism—i.e., Priority Charging Mechanism (PCM)—that charges BEBs simultaneously using the optimum number of chargers to minimize infrastructure costs. The model is evaluated using the actual transit network data of the city of Thunder Bay. ©2024 IEEE.

关键词： Charging stations

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Static Analysis Techniques for Fixing software Defects in MPI-Based Parallel Programs

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computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah Abdullah Al-Johany Sanaa Abdullah Sharaf Fathy Elbouraey Eassa Reem Abdulaziz Alnanih Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering and Distributed System Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed ***, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： High-performance computing parallel computing software engineering software defect message passing interface deadlock

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Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

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computers, Materials & Continua 2024年第4期79卷 713-733页

作者： Aizaz Ali Maqbool Khan Khalil Khan Rehan Ullah Khan Abdulrahman Aloraini Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan Software Competence Center Hagenberg Softwarepark 32aHagenberg4232Austria Department of Computer Science School of Engineering and Digital SciencesNazarbayev UniversityAstana010000Kazakhstan Department of Information Technology College of ComputerQassim UniversityP.O.Box 1162BuraydahSaudi Arabia

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse *** numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiv

关键词： Urdu sentiment analysis convolutional neural networks recurrent neural network deep learning natural language processing neural networks

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Automated Facial Expression Recognition and Age Estimation Using Deep Learning

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computers, Materials & Continua 2022年第6期71卷 5235-5252页

作者： Syeda Amna Rizwan Yazeed Yasin Ghadi Ahmad Jalal Kibum Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAbu Dhabi122612UAE Department of Human-Computer Interaction Hanyang UniversityAnsan15588Korea

With the advancement of computer vision techniques in surveillance systems,the need for more proficient,intelligent,and sustainable facial expressions and age recognition is *** main purpose of this study is to develop accurate facial expressions and an age recognition system that is capable of error-free recognition of human expression and age in both indoor and outdoor *** proposed system first takes an input image pre-process it and then detects faces in the entire *** that landmarks localization helps in the formation of synthetic face mask prediction.A novel set of features are extracted and passed to a classifier for the accurate classification of expressions and age *** proposed system is tested over two benchmark datasets,namely,the Gallagher collection person dataset and the Images of Groups *** system achieved remarkable results over these benchmark datasets about recognition accuracy and computational *** proposed system would also be applicable in different consumer application domains such as online business negotiations,consumer behavior analysis,E-learning environments,and emotion robotics.

关键词： Feature extraction face expression model local transform features and recurrent neural network(RNN)

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