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Enhancing User Experience in AI-Powered Human-computer Communication with Vocal Emotions Identification Using a Novel Deep Learning Method

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computers, Materials & Continua 2025年第2期82卷 2909-2929页

作者： Ahmed Alhussen Arshiya Sajid Ansari Mohammad Sajid Mohammadi Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Engineering and Information TechnologyOnaizah CollegesQassim51911Saudi Arabia

Voice, motion, and mimicry are naturalistic control modalities that have replaced text or display-driven control in human-computer communication (HCC). Specifically, the vocals contain a lot of knowledge, revealing details about the speaker’s goals and desires, as well as their internal condition. Certain vocal characteristics reveal the speaker’s mood, intention, and motivation, while word study assists the speaker’s demand to be understood. Voice emotion recognition has become an essential component of modern HCC networks. Integrating findings from the various disciplines involved in identifying vocal emotions is also challenging. Many sound analysis techniques were developed in the past. Learning about the development of artificial intelligence (AI), and especially Deep Learning (DL) technology, research incorporating real data is becoming increasingly common these days. Thus, this research presents a novel selfish herd optimization-tuned long/short-term memory (SHO-LSTM) strategy to identify vocal emotions in human communication. The RAVDESS public dataset is used to train the suggested SHO-LSTM technique. Mel-frequency cepstral coefficient (MFCC) and wiener filter (WF) techniques are used, respectively, to remove noise and extract features from the data. LSTM and SHO are applied to the extracted data to optimize the LSTM network’s parameters for effective emotion recognition. Python Software was used to execute our proposed framework. In the finding assessment phase, Numerous metrics are used to evaluate the proposed model’s detection capability, Such as F1-score (95%), precision (95%), recall (96%), and accuracy (97%). The suggested approach is tested on a Python platform, and the SHO-LSTM’s outcomes are contrasted with those of other previously conducted research. Based on comparative assessments, our suggested approach outperforms the current approaches in vocal emotion recognition.

关键词： Human-computer communication(HCC) vocal emotions live vocal artificial intelligence(AI) deep learning(DL) selfish herd optimization-tuned long/short K term memory(SHO-LSTM)

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CCGS-based discriminatory recognition of skeleton-based actions

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Multimedia Tools and Applications 2025年第6期84卷 3161-3174页

作者： Sameem, M Shujah Islam Department of Computer Science College of Computer Sciences & Information Technology King Faisal University Al Ahsa Saudi Arabia

As life forms feature joints, dynamic skeletons have been studied extensively as a potential tool for action detection. As a result of their focus on modelling skeletons, older methods were unable to adequately convey the whole range of motion at more erratic skeletal joints. Despite introducing spatial methods for unstructured data, detecting many simultaneous motions remains challenging. As part of this study, now article proposes a CCGS for skeleton-based action recognition by depicting skeletons organically on joints and therefore attempting to capture the spatiotemporal variation in the data. Let here propose a motion regression to learn the shape statistically from numerous data points since the representation of the joints is crucial to motion convolution. To achieve this sparsity in the underlying CCGS for efficient representation, now give spatial modelling of skeletons and formulate an optimisation problem with a slip-critical joint. Each joint has a strong or weak connection to the other joints in the same frame and to key joints in the action frames that have been processed. The coordinates of the skeleton sequence and the positions of the joints are then sent to the motion-caused graph, which has been tuned, so that features based on Chebyshev, spatial, and angular approximations of the motion may be learned. Also, the article examines the motion features approximation's representation of variation. The results of the experiments show that the recommended joints regression works and the proposed CCGS achieves state-of-the-art performance on the most popular datasets. The present research suggests a CCGS-based method for skeleton-based action recognition that captures spatiotemporal variations by intuitively depicting skeletons on joints. The experimental results suggest that the proposed method obtains state-of-the-art performance, with accuracy enhancements of up to 2% over existing methods on prominent datasets. © The Author(s), under exclusive licenc

关键词： Chebyshev approximation

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Comparison of Machine Learning Algorithms for Predicting Thyroid Disorders in Diabetic Patients

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第12期49卷 105-114页

作者： Sayyid, Hiba O. Mahmood, Salma A. Hamadi, Saad S. Department of Computer Science University of Basrah College of Computer Sciences and Information Technology Basrah Iraq Department of Intelligent Medical Systems University of Basrah College of Computer Sciences and Information Technology Basrah Iraq Department of Internal Medicine University of Basrah College of Medicine Basrah Iraq

Machine Learning (ML), a subfield of Artificial Intelligence (AI), has been used successfully in the healthcare domain for disease diagnosis. Thyroid disorders and diabetes are two of the most prevalent and interconnected chronic diseases, as both play critical roles in regulating various physiological processes in the body. This study aims to predict thyroid disorders in diabetes patients using six machine learning algorithms: Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machine (SVM). A locally sourced dataset comprising 44,539 instances of diabetic patients was utilized, undergoing preprocessing steps including data cleaning, encoding, and balancing. Two balancing techniques were employed: manual balancing and RandomUnderSampler. The dataset was partitioned into training and testing sets using a Stratified K-Fold cross-validation approach with 10 folds to ensure robust evaluation. Each algorithm’s performance was assessed using metrics such as accuracy and F1-score. Among the models, the RF algorithm outperformed the others, achieving the highest accuracy of 95% on the manually balanced dataset and 84% when the RandomUnderSampler technique was employed. Additionally, the F1-scores for RF were 95% and 82%, respectively, indicating its robustness in handling imbalanced datasets. This study highlights the importance of selecting appropriate preprocessing techniques and machine learning methods for healthcare datasets. The findings can assist healthcare providers in making early diagnoses and interventions for thyroid disorders in diabetic patients, potentially improving their quality of life and overall healthcare outcomes. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Logistic regression

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Privacy-preserving explainable AI: a survey

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Science China(information sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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A Novel Approach to Enhanced Cancelable Multi-Biometrics Personal Identification Based on Incremental Deep Learning

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computers, Materials & Continua 2025年第5期83卷 1727-1752页

作者： Ali Batouche Souham Meshoul Hadil Shaiba Mohamed Batouche Department of Computer and Information Sciences Northumbria UniversityLondonE17HTUK Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these *** of the key developments is the integration of deep learning techniques in biometric ***,despite these advancements,certain challenges *** of the most significant challenges is scalability over growing *** methods either require maintaining and securing a growing database,introducing serious security challenges,or relying on retraining the entiremodelwhen new data is introduced-a process that can be computationally expensive and *** challenge underscores the need for more efficient methods to scale *** this end,we introduce a novel approach that addresses these challenges by integrating multimodal biometrics,cancelable biometrics,and incremental learning *** work is among the first attempts to seamlessly incorporate deep cancelable biometrics with dynamic architectural updates,applied incrementally to the deep learning model as new users are enrolled,achieving high performance with minimal catastrophic *** leveraging a One-Dimensional Convolutional Neural Network(1D-CNN)architecture combined with a hybrid incremental learning approach,our system achieves high recognition accuracy,averaging 98.98% over incrementing datasets,while ensuring user privacy through cancelable templates generated via a pre-trained CNN model and random *** approach demonstrates remarkable adaptability,utilizing the least intrusive biometric traits like facial features and fingerprints,ensuring not only robust performance but also long-term serviceability.

关键词： Incremental learning personal identification cancelablemulti-biometrics pattern recognition security deep learning cyber-attacks transfer learning random projection catastrophic forgetting

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The Pivotal Role of Software Defined Networks to Safeguard Against Cyber Attacks: a Comprehensive Review

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PeerJ computer Science 2025年 11卷 1-36页

作者： Aljughaiman, Ahmed Almarri, Seetah Department of Computer Networks and Communications College of Computer Sciences and Information Technology King Faisal University Al-Ahsa Saudi Arabia

Software defined networks (SDNs) offer novel approaches to managing networks by separating the control plane from the data plane to enable programmable control over network resources effectively and dynamically. This framework supports monitoring of traffic flow and detection of threats while also enabling easy adaptation of network configurations, which is critical in safeguarding against cyber threats. However, this separation also brings forth security risks that cyber attackers may exploit. In this examination, the basic concepts of SDN are explained, pointing out their benefits compared to conventional networks and exploring the security issues that are part of SDN architectures. Different types of threats that focus on SDN layers are categorized and how they impact network security while suggesting different ways to address them. Furthermore, the review highlights issues and suggests potential research paths to enhance SDN security measures and ensure their effectiveness against ever-changing cyber dangers. © 2025 Aljughaiman and Almarri

关键词： Cyber attacks

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Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability

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Tungsten 2025年第1期7卷 137-151页

作者： Geethukrishnan Onkar Apte Kiran Kumar Tadi Energy Electrodics and Electrocatalysis (EEE) Lab Department of Chemistry School of Advanced Sciences Vellore Institute of Technology School of Computer Science and Engineering Vellore Institute of Technology

The precise detection and measurement of dopamine（DA）,a crucial neurotransmitter in the human body,plays a significant role in diagnosing,preventing,and treating neurological diseases associated with its levels.A highly sensitive DA electrochemical sensor was constructed by combining molybdenum disulfide quantum dots（MSQDs） with multiwalled carbon nanotubes（MWCNTs）.The MSQDs were synthesized using the shear exfoliation *** sensors consist of MSQDs with Mo-S edge catalytic centers for the DA redox reaction,and MWCNTs amplify the sensor *** linearity of the sensor for the detection of DA was tested in the presence of ascorbic acid(AA,50 μmol·L-1) and uric acid(UA,200 μmol·L-1),and exhibited linearity from 2 to 966 μmol·L-1of DA with 0.097 μA(mol·L-1)-1sensitivity and a low limit of detection of0.6 μmol·L-1（the ratio between signal and noise,S/N=3）.Moreover,the sensitivity and selectivity of the sensor were also studied using *** is no increase in amperometric current after adding the most potentially interfering *** sensor was successfully applied to recover DA in human blood sera ***,machine learning algorithms were operated to aid in the near-precise detection of DA in the heterogeneous mixture containing AA and *** algorithms facilitate the identification and quantification of DA amidst coexisting interferents,including AA,that are commonly present in biological matrices.

关键词： Dopamine MoS_2 Biosensor Multiwalled carbon nanotubes Machine learning Complex mixture

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ABL-SMOTE: A Novel Resampling Method by Handling noisy and borderline challenge for Imbalanced Dataset for Software Defect Prediction

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IEEE Access 2025年 13卷 78781-78794页

作者： Bashir, Kamal Ghorashi, Sara Abdelwahab Ahmed, Ali Khader, Abdolraheem Karary University College of Computer Science and Information Technology Department of Information Technology Omdurman12304 Sudan Princess Nourah bint Abdulrahman University P.O. Box 84428 Collage of Computer and Information Sciences Department of Computer Sciences Riyadh11671 Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Jeddah21589 Saudi Arabia Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Machine learning algorithms face important implementation difficulties due to imbalanced learning since the Synthetic Minority Oversampling Technique (SMOTE) helps improve performance through the creation of new minority class examples in feature space before preprocessing. The underlying problem leading to performance deterioration emerges from noise and boundary instances found in the minority class rather than an excessive imbalance ratio. Noise and marginal samples become problematic during oversampling operations since they reduce the performance of classification models. This paper proposes an advanced version of Borderline-SMOTE (BL-SMOTE), called ABL-SMOT approach that utilizes INFFC for noise filtering to optimize data quality in the dataset. This improvement method for minority class sample placement and noise filtering ensures higher classifier performance through ABL-SMOTE. The proposed technique receives assessment through the usage of software defect datasets along with multiple testing classifiers against conventional data sampling procedures. The experimental outcomes show that ABL-SMOTE performs better than alternative approaches in most tested datasets because it is an effective preprocessing tool for imbalanced classification problems. © 2013 IEEE.

关键词： Software testing

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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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ASAQ—Ant-Miner: optimized rule-based classifier

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Soft Computing 2025年第1期29卷 355-364页

作者： Ayub, Umair Almas, Bushra Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Institute of Information Technology Quaid-e-Azam University Islamabad Pakistan

Ant-Miner, a rule-based classifier, has been extensively utilized for classification tasks. However, it features numerous controlling parameters that significantly impact its performance. The standard Ant-Miner (AM) often encounters issues such as slow or premature convergence and high selective pressure. Several studies have proposed innovative methods to enhance AM’s performance by examining the quality function, heuristic strategies, and pheromone update mechanisms. Recent research has also focused on ant selection for pheromone updates and term selection strategies. Despite substantial efforts, existing studies are limited by challenges like local optima entrapment, premature or slow convergence, high selective pressure, exhaustive searches, and the generation of specific yet low-quality rules. This paper introduces a novel Ant-Miner algorithm (ASAQ—AM) that implements novel Ant-Selection strategies for pheromone updating and data classification, alongside an advanced Quality function. This novel quality function employs hierarchical thresholds to evaluate ants based on coverage, rule length, and accuracy. The selected ants contribute to pheromone updates on their respective paths guided by the proposed quality function. The effectiveness of the ASAQ—AM approach is assessed using four publicly available datasets and standard benchmark performance metrics, including accuracy and F1-score. The results demonstrate that the proposed method outperforms the basic Ant-Miner, state-of-the-art variants, and several data mining approaches in terms of accuracy, F1-score, and convergence speed. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Heuristic methods

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