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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Yasir Abdullah, R. Hariharan, V.C. Nithin, K.M. Swetha, V. Vijayaalayan, A. Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798350322842

Artificial Intelligence (AI) has become a popular tool to perform video surveillance in order to detect and identify humans, vehicles, objects, and events. By analyzing audio and images via computer software programs, AI model can identify people's faces and other emerging technologies that are commonly used in security monitoring systems. The main purpose of this study is to explore the different areas, where Artificial Intelligence (AI) can be used to detect and prevent fires. © 2023 IEEE.

关键词： Face recognition

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EmotionTracker: Real-time Facial Emotion Detection with OpenCV and DeepFace

EmotionTracker: Real-time Facial Emotion Detection with Open...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Bhanupriya, M. Kirubakaran, Namaskaram Jegadeeshwari, P. Chennai Institute of Technology Department of Computer Science and Business Systems Chennai India Chennai India

ISBN: (纸本)9798350348910

This paper introduces a real-time facial emotion detection and analysis system, leveraging a Streamlit-based web interface for user interaction and video processing. The motivation behind this research lies in the increasing relevance of facial emotion detection in diverse applications, including human-computer interaction and mental health monitoring. The project addresses the challenge of efficiently capturing, analyzing, and presenting emotional insights from user-uploaded videos. The primary problem tackled by the system is the need for a comprehensive and user-friendly solution to extract emotion-related information from facial expressions in real-time video streams. Employing a combination of OpenCV, DeepFace, and Streamlit, the system processes uploaded videos, detects facial emotions at each timestamp, and generates an emotion-annotated output. The methodology involves integrating OpenCV for video processing, DeepFace for emotion analysis, and Streamlit for an interactive user interface. Results showcase the system's ability to identify dominant emotions, quantify occurrences, and present the findings in an easily comprehensible manner. The implications of this work extend to various domains, such as enhancing human-computer interaction experiences and contributing to mental health research. The user-friendly interface and efficient emotion detection mechanisms make this system a valuable tool for researchers, practitioners, and developers seeking to explore and implement facial emotion analysis in real-world applications. © 2023 IEEE.

关键词： Emotion Recognition

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Exploring The Potential of Machine Learning for Early Diagnosis of Parkinson's Disease: A Comparative Study 4

Exploring The Potential of Machine Learning for Early Diagno...

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4th International Conference on Intelligent Engineering and Management, ICIEM 2023

作者： Devi Padma, K. Harshitha, P. Nafiza, R. Lilian, J. Felicia Malini, A. Thiagarajar College of Engineering Department of Computer Science and Business Systems Madurai625 015 India

ISBN: (纸本)9798350341126

The neurological ailment Parkinson's disease affects millions of individuals globally. Contrarily, an early detection of the illness will aid in its efficient treatment. With machine learning, which is still in its infancy, there are a number of prospects for computer-aided identification and diagnosis that may lessen inescapable medical issues and intrinsic clinical uncertainty, offer direction, and enhance decision-making. In an effort to improve the technique for diagnosing Parkinson's disease, we have proposed a model for early detection of the condition. Performance and speed were taken into consideration when XGBoost, a new machine learning technique, was developed. A decision tree-based technique called Extreme Gradient Boosting, or XGBoost, was developed. © 2023 IEEE.

关键词： Decision trees

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Feature Selection in Socio-Economic Analysis: A Multi-Method Approach for Accurate Predictive Outcomes

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International Journal of Crowd science 2025年第1期9卷 64-78页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Jebreen, Issam Nabot, Ahmad Rajab, Mohammed Alauthman, Mohammad Aldweesh, Amjad Aburub, Faisal Alangari, Someah Alzgol, Musab Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan University Headquarter University of Anbar Ramadi31001 Iraq Department of Information Security University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan Computer Information Systems Department Faculty of Information Technology Isra University Amman11622 Jordan

Feature selection is a cornerstone in advancing the accuracy and efficiency of predictive models, particularly in nuanced domains like socio-economic analysis. This study explores nine distinct feature selection methods, utilizing a heart disease dataset as a representative model for complex socio-economic systems. Our findings identified four universally recognized features as critical across all selection methods. However, the divergence in significance attributed to other features by different methods underscores the inherent variability in selection techniques. When the top four features were incorporated into twelve classification models, a noticeable surge in predictive accuracy was observed, emphasizing their foundational role in enhancing model outcomes. The variations among methods stress the need for a methodical and discerning approach to feature selection, especially in data-rich socio-economic landscapes. As we venture further into an era defined by data-driven decision-making, rigour and precision in feature selection become indispensable. Future research should extend this approach to broader datasets, ensuring the robustness and adaptability of our findings. © The author(s) 2025.

关键词： Prediction models

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Hybrid-Optimized Model for Deepfake Detection

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International Journal of Advanced computer science and Applications 2025年第4期16卷 148-160页

作者： Mancy, H. Elpeltagy, Marwa Eldahshan, Kamal Ismail, Aya Department of Computer Science College of Engineering and Computer Sciences Prince Sattam Bin Abdulaziz University Alkharj11942 Saudi Arabia Al-Azhar University Cairo Egypt Systems and Computers Department Al-Azhar University Egypt Mathematics Department Faculty of Science Al-Azhar University Egypt Mathematics Department Tanta University Egypt

The advancement of deep learning models has led to the creation of novel techniques for image and video synthesis. One such technique is the deepfake, which swaps faces among persons and then produces hyper-realistic videos of individuals saying or doing things that they never said or done. These deepfake videos pose a serious risk to everyone and countries if they are exploited for extortion, scamming, political disinformation, or identity theft. This work presents a new methodology based on a hybrid-optimized model for detecting deepfake videos. A Mask Region-based Convolutional Neural Network (Mask R-CNN) is employed to detect human faces from video frames. Then, the optimal bounding box representing the face region per frame is selected, which could help to discover many artifacts. An improved Xception-Network is proposed to extract informative and deep hierarchical representations of the produced face frames. The Bayesian optimization (BO) algorithm is employed to search for the optimal hyperparameters' values in the extreme gradient boosting (XGBoost) classifier model to properly discriminate the deepfake videos from the genuine ones. The proposed method is trained and validated on two different datasets;CelebDF-FaceForencics++ (c23) and FakeAVCeleb, and tested also on various datasets;CelebDF, DeepfakeTIMIT, and FakeAVCeleb. The experimental study proves the superiority of the proposed method over the state-of-the-art methods. The proposed method yielded %97.88 accuracy and %97.65 AUROC on the trained CelebDF-FaceForencics++ (c23) and tested CelebDF datasets. Additionally, it achieved %98.44 accuracy and %98.44 AUROC on the trained CelebDF-FaceForencics++ (c23) and tested DeepfakeTIMIT datasets. Moreover, it yielded %99.50 accuracy and %99.21 AUROC on the FakeAVCeleb visual dataset. © (2025), (science and Information Organization). All Rights Reserved.

关键词： Convolutional neural networks

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An Intelligent Decision Support System for Forecasting Financially Distressed businesses 11

An Intelligent Decision Support System for Forecasting Finan...

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11th IEEE International Conference on Intelligent Computing and Information systems, ICICIS 2023

作者： Abdul-Kareem, Ahmed Amer Taha Fayed, Zaki Rady, Sherine El-Regaily, Salsabil Amin Nema, Bashar M. Ain Shams University Faculty of Computer and Information Sciences Computer Science Department Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Information Systems Department Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Basic Science Department Cairo Egypt Mustansiriyah University Faculty of Sciences Computer Science Department Baghdad Iraq

ISBN: (纸本)9798350322101

Many companies have collapsed in recent years due to the economic downturn and the COVID-19 epidemic, both of which have severely harmed them. A model of this kind can serve as an early warning system, alerting decision-makers to potential problems with their finances so they can take preventative action before their situation worsens. Forecasting Financially Distressed businesses (FFDB) is the primary goal of this study. The most significant finding of this study is that Extreme Gradient Boosting (XGBoost) outperforms other popular machine learning models. To speed up the forecasting process, it was decided to utilize a method called Particle Swarm Optimization (PSO) to reduce the number of features used in the model. On the Polish, and Qualitative Bankruptcy Prediction Datasets, five models were tested and judged. In comparison to other learning models, the experimental findings showed that the PSO-FS-XGBoost model performed very well, with an accuracy rate of 100% and the best overall Area Under the Curve (AUC) of this framework reaching 100%. The feature selection in the PSO-FS-XGBoost model boosted accuracy by 4%, sped up the model by 3.74 times, and reduced the feature space by up to 30%. © 2023 IEEE.

关键词： Forecasting

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DeepLeaf: an optimized deep learning approach for automated recognition of grapevine leaf diseases

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Neural Computing and Applications 2025年第15期37卷 8799-8823页

作者： Talaat, Fatma M. Shams, Mahmoud Y. Gamel, Samah A. ZainEldin, Hanaa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Faculty of Engineering Horus University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Plant diseases can cause severe losses in agricultural production, impacting food security and safety. Early detection of plant diseases is crucial to minimize crop damage and ensure agricultural sustainability. Manual monitoring is often impractical due to the complexity and time involved, making automated disease recognition essential. This study presents a new Plant Disease Detection Algorithm (PDDA) called DeepLeaf focused on identifying four common grapevine diseases: leaf blight, black rot, stable, and black measles. The PDDA integrates three key modules: an Image Preprocessing Module, a Feature Extraction Module, and an Optimized Convolutional Neural Network (OCNN)-based Classification Module. The OCNN forms the core of the classification system, with its hyperparameters fine-tuned using fuzzy optimization to enhance performance. Preprocessing techniques are applied to analyze diseased leaves, and a logistic regression algorithm is used to downsample the features for better analysis. The CNN is trained on images from the Plant Village dataset, allowing it to detect and classify grapevine leaf diseases accurately. The proposed model's efficiency in the automated diagnosis of grapevine diseases is demonstrated by its remarkable 99.7% accuracy rate. This high accuracy indicates that the PDDA may help with more effective and scalable plant disease monitoring, which will ultimately allow better agricultural practices. © The Author(s) 2025.

关键词： CNN Detection of leaf disease Extracting features Plant disease

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Textual Emotion Recognition: Advancements and Insights 6

Textual Emotion Recognition: Advancements and Insights

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Esmail, Ahmed A. Abdallah, Sayed M. Elsayed, Mohamed A. Ghaith, Amr E. Elbanhawy, Magy Al-Zoghby, Aya M. Moawad, Ibrahim F. Artificial Intelligence Science New Mansoura University Faculty of Computer Science and Engineering Dakhlia Egypt Mansoura University Faculty of Computers and Information Dakhlia Egypt New Mansoura University Faculty of Computer Science and Engineering Dakhlia Egypt Portsaid University Faculty of Engineering Egypt Damietta University Faculty of Computers and Artificial Intelligence Computer Science Department Damietta Egypt Ain Shams University Information Systems Faculty of Computer and Information Sciences Cairo Egypt College of Computer Science and Engineering Taibah University Department of Artificial Intelligence Medina42353 Saudi Arabia

ISBN: (纸本)9798350378511

In recent years, there has been a significant increase in attention toward emotion detection in text analysis, driven by its broad applications across marketing, political science, psychology, human-computer interaction, and artificial intelligence. This growing interest is primarily due to the critical role of textual expression as a repository of human emotions and sentiments. The development of sophisticated natural language processing (NLP) techniques has emphasized the importance of exploring emotion detection and recognition within textual data. By utilizing a wide range of sources, including social media content, microblogs, news articles, and customer feedback, text mining aims to reveal the underlying emotional currents within the text. However, existing models often struggle to capture the complicated emotional nuances woven into words. Addressing this challenge, the innovative semantic emotion neural network (SENN) architecture has been introduced. The SENN model marks a significant advancement, featuring two synergistic sub-networks: a bidirectional long short-term memory (BiLSTM) network that extracts contextual information and a convolutional neural network (CNN) that analyzes and extracts emotional features, highlighting the text's intrinsic emotional connections. The SENN model's performance has been thoroughly evaluated on widely used real-world datasets, benchmarked against Ekman's six fundamental emotions. Results demonstrated its superiority, showing that the SENN model excels in emotion recognition accuracy and quality in conjunction with additional techniques. It also holds potential for enhancement by incorporating more comprehensive emotional word embedding, suggesting a promising future for text-based emotion analysis. The proposed paper presents goals for detecting sentiment in text data and introduces a novel architecture that effectively captures the complexity of emotional nuances. We create an abstract model and compare three types of m

关键词： Emotion Recognition

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Observational Equivalence of Conditional Belief Bases 36th

Observational Equivalence of Conditional Belief Bases

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36th International Florida Artificial Intelligence Research Society Conference, FLAIRS-36 2023

作者： Beierle, Christoph Haldimann, Jonas Schwarzer, Leon Knowledge-Based Systems Faculty of Mathematics and Computer Science FernUniversit Hagen58084 Germany

In nonmonotonic reasoning, a conditional of the form ‘If A then usually B' is typically accepted if a situation where both A and B hold is deemed to be more plausible, more probable, or less surprising, etc., than a situation where A holds, but B does not hold. In a propositional setting, this leads to a relation on the propositional interpretations, also called worlds, by comparing worlds according to their plausibility. In this paper, we address the question of which kind of relations on the set of worlds can be obtained by completing a conditional belief base via an inductive inference operator. As a key concept for our investigations, we introduce and employ the notion of observational equivalence of belief bases that takes an inductive inference operator and a set of queries into account. This leads to the notion of the observational normal form (ONF) and, by focussing on so-called base conditionals, to the base conditional normal form (BCNF). The acceptance of base conditionals corresponds to the plausibility ordering on possible worlds induced by an inference method. Both normal forms ONF and BCNF are combined with renamings as an additional dimension. We establish the interrelationships among the normal forms and evaluate them empirically with respect to systematically generated belief bases. © 2023 by the authors. All rights reserved.

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Hybrid Parallel Architecture Integrating FFN, 1D CNN, and LSTM for Predicting Wildfire Occurrences in Morocco 9th

Hybrid Parallel Architecture Integrating FFN, 1D CNN, and L...

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9th International Conference on Smart City Applications, SCA 2024

作者： Jadouli, Ayoub El Amrani, Chaker Computer Science and Smart Systems Faculty of Sciences and Technology Abdelmalek Essaâdi University Tangier Morocco

ISBN: (纸本)9783031886522

A novel "Hybrid Parallel Architecture Integrating FFN, 1D CNN, and LSTM" is presented to enhance wildfire prediction capabilities in Morocco. Utilizing the "Morocco Wildfire Predictions: 2010–2022 ML Dataset", this model merges various data points, including meteorological conditions, human population density, and environmental factors like soil moisture and vegetation indices. The architecture combines feedforward neural networks (FFN), one-dimensional convolutional neural networks (1D CNN), and long short-term memory networks (LSTM) to process different subsets of features in parallel, capturing both spatial and temporal dependencies crucial for accurate wildfire predictions. The dataset covers data from 2010 to 2022 and includes standardized features specifically prepared for machine learning applications in disaster management. By training this hybrid model, significant validation accuracy was observed, indicating robust performance. Initial results were promising, with the model achieving an accuracy of 83.96% on the training set and 87.19% on the validation set during the first epoch, improving to 87.13% on the training set and 87.56% on the validation set by the second epoch. These results underscore the efficacy of integrating multiple neural network architectures to enhance predictive accuracy in critical applications such as wildfire prediction. The findings suggest substantial potential for deploying such models in real-world scenarios, aiding policymakers and disaster management teams in deploying timely and effective responses to mitigate wildfire impacts. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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