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Automatic emotion recognition using deep neural network

Multimedia Tools and Applications 2025年 1-30页

作者： Sujatha, R. Chatterjee, Jyotir Moy Pathy, Baibhav Hu, Yu-Chen School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of CSE Graphic Era University Dehradun India School of Electrical Engineering Vellore Institute of Technology Vellore India Department of Computer Science Tunghai University Taichung City Taiwan

Emotions are a vital semantic part of human correspondence. Emotions are significant for human correspondence as well as basic for human–computer cooperation. Viable correspondence between people is possibly achieved when both the importance and the emotion of the correspondence are perceived by all groups included. Understanding the significance of language has generally been concentrated on in natural language processing (NLP) as a semantic examination. In NLP, the text can be handled appropriately for classification. Emotion detection from facial emotion is the subfield of social signal processing applied in a wide assortment of regions, explicitly for human and PC collaboration. Many researchers have proposed various approaches, generally utilizing machine learning concepts. Automatic emotion recognition (AER) is significant for working with consistent intuitiveness between a person and a smart device toward fully acknowledging an intelligent society. Many researchers examined cross-lingual and multilingual speech emotion as a stage toward language-free emotion acknowledgment in natural speech. In the present work, we are proposing a deep learning-based AER system using four openly accessible datasets, namely Basic Arabic Vocal Emotions Dataset (BAVED), Acted Emotional Speech Dynamic Database (AESDD), Urdu written in Latin/Roman Script (URDU), and Toronto Emotional Speech Set (TESS), by utilizing the Jupyter notebook and a Python library for music and audio synthesis named Librosa. The experimental results exhibited that the proposed approach achieves better than the existing approaches, i.e., the accuracy of the proposed system with the URDU dataset is 96.24%, the TESS dataset is 99.10%, the AESDD dataset is 65.97%, and the BAVED dataset is 73.12%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantics

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Hybridization of deep learning models with crested porcupine optimizer algorithm-based cybersecurity detection on industrial IoT for smart city environments

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Alexandria Engineering Journal 2025年 127卷 239-250页

作者： Alzakari, Sarah A. Aljebreen, Mohammed Asiri, Mashael M. MANSOURI, Wahida Alahmari, Sultan Alqahtani, Mohammed Sorour, Shaymaa Bedewi, Wafi Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science Community College King Saud University P.O. Box 28095 Riyadh11437 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science and Information Technology Faculty of Sciences and arts Turaif Northern Border University Arar91431 Saudi Arabia Cybersecurity Institute Riyadh Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Management Information Systems School of Business King Faisal University Al-Ahsa31982 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Smart cities have attracted extensive coverage from multidisciplinary studies, and many artificial intelligence (AI) solutions have been designed. Conversely, cybersecurity has constantly been a crucial issue and is becoming gradually dangerous in smart cities. The attack defence models are inappropriate for perceiving multistep assaults as the recognition rules are restricted, and efficacy is partial due to many false security alarms. Therefore, an innovative solution is immediately required to progress cybersecurity defence ability. Machine learning (ML) methods are commonly employed to recognize numerous attacks because they could help network administrators grab analogous initials to avert intrusion. This study presents Cybersecurity using a Crested Porcupine Optimizer Algorithm with Hybrid Deep Learning Models (CCPOA-HDLM). The foremost intention of this study is to improve cybersecurity detection and classification in smart city environments. To accomplish that, the CCPOA-HDLM method comprises distinct processes such as min-max normalization, improved Salp swarm algorithm (ISSA)-based feature selection, Multi-Channel Convolutional Neural Network - Recurrent Neural Network (MCNN-RNN)-based cybersecurity detection, and crested porcupine optimizer (CPO)-based parameter selection process. Primarily, data normalization utilizing min-max normalization is implemented. Next, the CCPOA-HDLM method utilizes ISSA based feature selection method to select optimum features. The CCPOA-HDLM technique employs a hybrid of the MCNN-RNN model for the cybersecurity detection and classification process. Moreover, the hyperparameter range of the hybrid of DL techniques occurs using the CPO technique. The experimental validation of the CCPOA-HDLM approach is performed on the UNSW-NB15 dataset, and the outcome portrayed a superior accuracy value of 99.04 % over other recent approaches under various measures. © 2025 The Authors

关键词： Convolutional neural networks

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Designing and Analyzing Green Networking Solutions in Data Centers Using AI for Energy Optimization

Designing and Analyzing Green Networking Solutions in Data C...

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Machine Learning and Autonomous systems (ICMLAS), International Conference on

作者： Sahar Ebadinezhad Alwaled Khalid Shaalan Shaalan Noor Khattab Omar Algailani Mohammed Al-Hussein Charles Zulu Yonmah Department of Computer Information Systems Computer Information Systems Research and Technology Center(CISRTC) Near East University Cyprus Nicosia Turkey Department of Computer Information Systems Near East University Nicosia Cyprus

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

A significant obstacle in the world of information IT, especially in terms of energy efficiency, is the increasing demand for energy from data centers. These massive plants currently consume nearly 1% of the world's electricity, and this percentage is expected to increase significantly in the coming decades. Therefore, it has now become imperative to identify effective ways to maximize the energy use of data centers, which has led to the study of the use of artificial intelligence to come up with creative solutions. The purpose of this research is to use AI to optimize energy use in data centers by designing and analyzing green networking solutions. It entails using AI methods like neural networks and reinforcement learning to increase data centers' energy efficiency and lessen their environmental effect. The study specifically focuses on enhancing important characteristics, including cooling systems and Power Usage Effectiveness (PUE). This study aims to replace conventional, static energy management techniques with adaptive strategies that react to current facts and anticipated energy demands by utilizing artificial intelligence. It is anticipated that the suggested solutions will significantly increase energy efficiency, lessen their negative effects on the environment, and help data centers become more sustainable.

关键词： Data centers Energy consumption Renewable energy sources Green communications Reinforcement learning Energy efficiency Data models Artificial intelligence Sustainable development Optimization

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Converging paths to Notes In search of computer-based information systems in a networked company

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information Technology & People 1995年第1期8卷 7-34页

作者： Karsten, Helena Department of Computer Science and Information Systems University of Jyväskylä United States

The nature of the information systems needed in a networked firm isnot well known: nor is the process leading to particular *** a small computer‐consulting company which works as a networkof independent consultants, and analyses the slow and circuitous pathfrom recognizing its IS/IT needs to its commitment to a particularsolution as seven steps; using ideas of structuration theory as a *** the selection process, participants formed new interpretations ofthe firm, of technology, and of the appropriate process towards afeasible solution. New resources – both material and symbolic– were acquired to enable the search. Existing “norms”provided boundaries for the search process, but at times had to yield orbe circumvented in order for the organization to reach a solution.

关键词： Interpretational communications Multimedia

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Intrusion Detection in NSL-KDD Dataset Using Hybrid Self-Organizing Map Model

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computer Modeling in Engineering & Sciences 2025年第4期143卷 639-671页

作者： Noveela Iftikhar Mujeeb Ur Rehman Mumtaz Ali Shah Mohammed J.F.Alenazi Jehad Ali Knowledge Unit of Systems and Technology University of Management and TechnologySialkot51310Pakistan Department of Computer Science University ofWahWah Cantt47010Pakistan Department of Computer Engineering College of Computer and Information Sciences(CCIS)King Saud UniversityRiyadh11451Saudi Arabia Department of AI Convergence Network Ajou UniversitySuwon16499Republic of Korea

Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few *** devices are now easy targets for hackers because of their built-in security *** a Self-Organizing Map(SOM)hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting(XGBoost)for multi-class classification can improve network traffic intrusion *** proposed model is evaluated on the NSL-KDD *** hybrid approach outperforms the baseline line models,Multilayer perceptron model,and SOM-KNN(k-nearest neighbors)model in precision,recall,and F1-score,highlighting the proposed approach’s scalability,potential,adaptability,and real-world ***,this paper proposes a highly efficient deployment strategy for resource-constrained network *** results reveal that Precision,Recall,and F1-scores rise 10%-30% for the benign,probing,and Denial of Service(DoS)*** particular,the DoS,probe,and benign classes improved their F1-scores by 7.91%,32.62%,and 12.45%,respectively.

关键词： Intrusion detection self-organizing map Internet of Things dimensionality reduction

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Ordered Clustering-Based Semantic Music Recommender System Using Deep Learning Selection

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computers, Materials & Continua 2025年第5期83卷 3025-3057页

作者： Weitao Ha Sheng Gang Yahya D.Navaei Abubakar S.Gezawa Yaser A.Nanehkaran School of Computer Science and Technology Weinan Normal UniversityWeinan714099China School of Information&Engineering Yancheng Teachers UniversityYancheng224002China Department of Computer and Technology Engineering Qazvin BranchIslamic Azad UniversityQazvin34199-15195Iran School of Information Engineering Sanming UniversitySanming365004China Department ofManagement Information Systems Faculty of Economics and Administrative SciencesCankayaUniversityAnkara06790Türkiye

Music recommendation systems are essential due to the vast amount of music available on streaming platforms,which can overwhelm users trying to find new tracks that match their *** systems analyze users’emotional responses,listening habits,and personal preferences to provide personalized suggestions.A significant challenge they face is the“cold start”problem,where new users have no past interactions to guide *** improve user experience,these systems aimto effectively recommendmusic even to such users by considering their listening behavior and music *** paper introduces a novel music recommendation system that combines order clustering and a convolutional neural network,utilizing user comments and rankings as ***,the system organizes users into clusters based on semantic similarity,followed by the utilization of their rating similarities as input for the convolutional neural *** network then predicts ratings for unreviewed music by ***,the system analyses user music listening behaviour and music *** popularity can help to address cold start users as ***,the proposed method recommends unreviewed music based on predicted high rankings and popularity,taking into account each user’s music listening *** proposed method combines predicted high rankings and popularity by first selecting popular unreviewedmusic that themodel predicts to have the highest ratings for each *** these,the most popular tracks are prioritized,defined by metrics such as frequency of listening across *** number of recommended tracks is aligned with each user’s typical listening *** experimental findings demonstrate that the new method outperformed other classification techniques and prior recommendation systems,yielding a mean absolute error(MAE)rate and rootmean square error(RMSE)rate of approximately 0.0017,a hit rate of 82.45%,an average normalized discounted cumulative gain

关键词： Music recommender system order clustering deep learning

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Mango Disease Detection Using Fused Vision Transformer with ConvNeXt Architecture

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computers, Materials & Continua 2025年第4期83卷 1023-1039页

作者： Faten S.Alamri Tariq Sadad Ahmed S.Almasoud Raja Atif Aurangzeb Amjad Khan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan AIDA Lab Department of Information SystemsCollege of Computer&Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia Department of Computer Science International Islamic UniversityIslamabad44000Pakistan

Mango farming significantly contributes to the economy,particularly in developing ***,mango trees are susceptible to various diseases caused by fungi,viruses,and bacteria,and diagnosing these diseases at an early stage is crucial to prevent their spread,which can lead to substantial *** development of deep learning models for detecting crop diseases is an active area of research in smart *** study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer(ViT)*** datasets were *** first,MangoLeafBD,contains data for mango leaf diseases such as anthracnose,bacterial canker,gall midge,and powdery *** second,SenMangoFruitDDS,includes data for mango fruit diseases such as Alternaria,Anthracnose,Black Mould Rot,Healthy,and Stem and *** datasets were obtained from publicly available *** proposed model achieved an accuracy of 99.87%on the MangoLeafBD dataset and 98.40%on the MangoFruitDDS *** results demonstrate that ConvNeXt and ViT models can effectively diagnose mango diseases,enabling farmers to identify these conditions more *** system contributes to increased mango production and minimizes economic losses by reducing the time and effort needed for manual ***,the proposed system is integrated into a mobile application that utilizes the model as a backend to detect mango diseases instantly.

关键词： ConvNeXt model fusion mango disease smart agriculture vision transformer

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Lower Bounds for Approximate (& Exact) k-Disjoint-Shortest-Paths 22nd

Lower Bounds for Approximate (& Exact) k-Disjoint-Shortest-...

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22nd International Workshop on Approximation and Online Algorithms, WAOA 2024

作者： Chitnis, Rajesh Thomas, Samuel Wirth, Anthony School of Computer Science The University of Birmingham Birmingham United Kingdom School of Computing and Information Systems The University of Melbourne Melbourne Australia School of Computer Science The University of Sydney Sydney Australia

ISBN: (纸本)9783031813955

Given a graph G=(V,E) and a set T={(si,ti):1≤i≤k}⊆V×V of k pairs, the k-Vertex-Disjoint-Paths (resp. k-Edge-Disjoint-Paths) problem asks to determine whether there exist k pairwise vertex-disjoint (resp. edge-disjoint) paths P1,P2,…,Pk in G such that, for each 1≤i≤k, Pi connects si to ti. Both the edge-disjoint and vertex-disjoint versions in undirected graphs are famously known to be FPT (parameterized by k) due to the Graph Minor Theory of Robertson and Seymour. Eilam-Tzoreff [DAM ‘98] introduced a variant, known as the k-Disjoint-Shortest-Paths problem, where each path is further required to be a shortest path connecting its pair. They showed that the k-Disjoint-Shortest-Paths problem is NP-complete on both directed and undirected graphs;this holds even if the graphs are planar and have unit edge lengths. We focus on four versions of the problem, corresponding to considering edge/vertex disjointness, and to considering directed/undirected graphs. Building on the reduction of Chitnis [SIDMA ’23] for k-Edge-Disjoint-Paths on planar DAGs, we obtain the following inapproximability lower bound for each of the four versions of k-Disjoint-Shortest-Paths on n-vertex graphs:Under the gap version of the Exponential Time Hypothesis (Gap-ETH), there exists a constant δ>0 such that for any constant 0δ·k time. Under the gap version of the Exponential Time Hypothesis (Gap-ETH), there exists a constant δ>0 such that for any constant 0δ·k time. We provide a single, unified framework to obtain lower bounds for each of the four versions of k-Disjoint-Shortest-Paths. We are able to further strengthen our results by restricting the structure of the input graphs in the lower bound constructions as follows:Directed: The inapproximability lower bound for edge-disjoint (resp. vertex-disjoint) paths holds even if the input graph is a planar (resp. 1-planar) DAG with max in-degree and max out-degree at most ***: The inapproximability lower bound for edge-disjoint (resp. vertex-dis

关键词： Undirected graphs

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Advanced Air Mobility: Innovations, Applications, Challenges, and Future Potential

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Journal of Air Transportation 2025年第2期33卷 169-187页

作者： Raza, Waleed Renkhoff, Justus Ogirimah, Olayemi Bawa, Gurvir Kaur Stansbury, Richard S. Department of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona BeachFL32114 United States Department of Information Systems University of Maryland BaltimoreMD21250 United States

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Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning

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computers in Biology and Medicine 2025年 186卷 109703-109703页

作者： Shaikh, Asadullah Amin, Samina Zeb, Muhammad Ali Sulaiman, Adel Al Reshan, Mana Saleh Alshahrani, Hani Department of Information Systems College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia Institute of Computing Kohat University of Science and Technology Kohat26000 Pakistan Department of Computer Science College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia

– Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmentation, yet misdiagnoses yield effective medical responses, impacting patient survival rates. Recent technological advancements have popularized deep learning-based medical image analysis, leveraging transfer learning to reuse pre-trained models for various applications. BT segmentation with MRI remains challenging despite advancements in image acquisition techniques. Accurate detection and segmentation are essential for proper diagnosis and treatment planning. This study aims to enhance BT detection and segmentation accuracy and effectiveness of categorization through the implementation of an advanced stacking ensemble learning (SEL) approach. This study explores the efficiency of SEL architecture in augmenting the precision of BT segmentation. SEL, a prominent approach within the machine learning paradigm, combines the predictions of base-level models and improves the overall performance of predictions in order to reduce the errors and biases of each model. The proposed approach involves designing a stacked DenseNet201 as the meta-model called SEL-DenseNet201, complemented by six diverse base models such as mobile network version 3 (MobileNet-v3), 3-dimensional convolutional neural network (3D-CNN), visual geometry group network with 16 and 19 layers (VGG-16 and VGG-19), residual network with 50 layers (ResNet50), and Alex network (AlexNet). The strengths of the base models are calculated to capture distinct aspects of the BT MRI, aiming for enhanced segmentation performance. The proposed SEL-DenseNet201 is trained using BT MRI datasets. The augmentation techniques are applied to MRI scans to balance and enhance the model performance through the application of image enhancement and segmentation techniques. The proposed S

关键词： Convolutional neural networks

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