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Emotion Recognition System via Facial Expressions and Speech Using Machine Learning and Deep Learning Techniques

SN Computer Science 2023年第4期4卷 1-10页

作者： Chaudhari, Aayushi Bhatt, Chintan Nguyen, Thanh Thi Patel, Nisarg Chavda, Kirtan Sarda, Kalind U & P U. Patel Department of Computer Engineering Chandubhai S. Patel Institute of Technology Charotar University of Science And Technology (CHARUSAT) Gujarat Changa 388421 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Knowledge Corridor Gujarat Gandhinagar 382007 India School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Victoria 3216 Australia Bishop University 2600 College St Sherbrooke J1M 1Z7 QC Canada Crest Data Systems Private Limited Gandhinagar Highway Makarba Gujarat Ahmedabad 380051 India Programmer Analyst Meditab Software Pvt Ltd Kalasagar Mall Ghatlodiya Gujarat Ahmedabad 380061 India

Patients in hospitals frequently exhibit psychological issues such as sadness, pessimism, eccentricity, and anxiety. However, hospitals normally lack tools and facilities to continuously monitor the psychological health of patients. It is desirable to identify depression in patients so that it can be managed by instantly providing better therapy. This can be possible by advances in machine learning for image processing with notable applications in the domain of emotion recognition using facial expressions. In this paper, we have proposed two different methods, i.e. facial expression detection and voice analysis, to predict emotions. For facial expression recognition, we have used two approaches, one is the use of Gabor filters for feature extraction with support vector machine for classification and another is using convolutional neural network (CNN). For voice analysis, we extracted mel-frequency cepstral coefficients from speech data and, based on those features, predicted the emotions of the speech using a CNN model. Experimental results show that our proposed emotion recognition methods obtained high accuracy and thus could be potentially deployed to real-world applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep learning Expressions Facial emotion Machine learning Speech SVM

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Classification or Prompting: A Case Study on Legal Requirements Traceability

arXiv

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arXiv 2025年

作者： Etezadi, Romina Abualhaija, Sallam Arora, Chetan Briand, Lionel School of Electrical Engineering & Computer Science University of Ottawa Canada SnT Centre for Security Reliability and Trust University of Luxembourg Luxembourg Faculty of Information Technology Monash University Melbourne Australia Lero Research Ireland centre for Software Research University of Limerick Ireland

New regulations are continuously introduced to ensure that software development complies with the ethical concerns and prioritizes public safety. A prerequisite for demonstrating compliance involves tracing software requirements to legal provisions. Requirements traceability is a fundamental task where requirements engineers are supposed to analyze technical requirements against target artifacts, often under limited time budget. Doing this analysis manually for complex systems with hundreds of requirements is infeasible. The legal dimension introduces additional challenges that only exacerbate manual effort. In this paper, we investigate two automated solutions based on large language models (LLMs) to predict trace links between requirements and legal provisions. The first solution, Kashιf, is a classifier that leverages sentence transformers and semantic similarity. The second solution prompts a recent generative LLM based on Rice, a prompt engineering framework. On a benchmark dataset, we empirically evaluate Kashιf and compare it against a baseline classifier from the literature. Kashιf can identify trace links with an average recall of ≈67%, outperforming the baseline with a substantial gain of 54 percentage points (pp) in recall. However, on unseen, more complex requirements documents traced to the European general data protection regulation (GDPR), Kashιf performs poorly, yielding an average recall of 15%. On the same documents, however, our Rice-based solution yields an average recall of 84%, with a remarkable gain of about 69 pp over Kashιf. Our results © 2025, CC BY.

关键词： Semantics

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A novel deep learning approach for investigating liquid fuel injection in combustion system

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Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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Experimental Evaluation of Cross-Platform Recognition of Identical and Unknown Users over Social Networking Environment

Experimental Evaluation of Cross-Platform Recognition of Ide...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Kancharla Nagababu S. Ananthi Neeraj Sunheriya Venkata Siva Prasad Ch J. Jayanthi Hamed Fawareh Department of Computer Applications Aditya University Department of CSE St. Joseph's College of Engineering Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of ECE Jeppiaar Engineering College Chennai-119 Department of CSE Sona College of Technology Salem Software Engineering Dept Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The capacity to individually identify and categorize people across sites and apps is vital for trustworthiness, fraud prevention, and personalized experiences in online social networking. In this study, strategies for platform identification among similar and unknown social networking users are evaluated. We study strategies that employ user attributes, behavior history, and inter-network connections to match profiles on multiple platforms and uncover new, previously connected people. This cross-validation on the proposed method uses Social Linkage Identification Strategy (SLIS) and Friend Relationship Evaluator. According to quantitative experiment results, our approach improves search people across multiple social networks more than FRE. The extensive social connection web in many online social networks is used to enhance user identification and verification via the Friend Relationship Evaluator (FRE) system. Our process uses advanced machine learning and data fusion to handle a wide variety of natural ventilation phenomena from different sensors and platform formats. We evaluate attribute-based matching, graph approaches, and behavior analytics for accuracy, scalability, and computing cost. This study advances user identification systems across digital platforms and helps service providers and end-users establish more sophisticated online identity management strategies.

关键词： Couplings Accuracy Social networking (online) Scalability Sensor phenomena and characterization Sensor fusion Ventilation User experience Protection Standards

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Intelligent photonic crystal-based optical sensor for accurate glycerol-water mixture measurement using artificial neural networks

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Optical Materials Express 2025年第7期15卷 1710-1729页

作者： Saeed Roshani Salah I Yahya Pouya Karami Muhammad Akmal Chaudhary Maher Assaad Fariborz Parin Fawwaz Hazzazi Fawnizu Azmadi Hussin Sobhan Roshani Department of Electrical Engineering Ker.C. Islamic Azad University Kermanshah Iran Department of Software Engineering Faculty of Engineering Koya University Koya KOY45 Iraq Department of Computer Technology Engineering College of Technical Engineering Al-Hadba University Mosul Iraq Department of Electrical and Computer Engineering College of Engineering and Information Technology Ajman University Ajman 346 United Arab Emirates Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj 11492 Saudi Arabia Department of Electrical and Electronic Engineering Universiti Teknologi PETRONAS Seri Iskandar 32610 Malaysia

This paper proposes an intelligent photonic crystal-based optical sensor designed for the first time to accurately measure glycerol-water concentration and temperature. The proposed sensor features a novel two-dimensional (2D) photonic crystal structure with an optimized waveguide configuration to enhance refractive index sensitivity. The sensor structure does not include defect rods, which simplifies fabrication and enhances stability. By using the unique optical properties of photonic crystals and the artificial neural network (ANN), the proposed design ensures high precision and stability in detecting changes in the glycerol concentration. The performance of the sensor was evaluated based on sensitivity, detection limit (DL), figure of merit (FOM), and quality factor (Q-F) across different temperatures and glycerol concentrations. The optical response of the sensor was numerically analyzed and simulated using the finite-difference time-domain (FDTD) method. Then, a feedforward ANN model was developed and trained to predict glycerol concentration and temperature from the output spectral data, enabling intelligent and real-time analysis. The results demonstrate that the proposed sensor achieves high sensitivity (up to 89.9 nm/RIU), a low detection limit (0.0003–0.0010 RIU −1 ), and an excellent Q-factor (5233), making it a highly effective solution for refractive index sensing. Overall, the findings confirm that the proposed photonic crystal sensor, enhanced with ANN-based intelligent analysis, offers high accuracy, stability, and fast response, making it suitable for biomedical, pharmaceutical, and industrial applications where precise glycerol concentration measurements are required.

关键词： Effective refractive index Nematic liquid crystals Neural networks Photonic crystal sensors Photonic crystals Sensor performance

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Smart Healthcare: A Logical Development of Medical Image Processing Based Sleep Apnea Disease Detection Using Elevated Neural Classification Principle

Smart Healthcare: A Logical Development of Medical Image Pro...

引用

Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Jagadeesh. P Radhamani. V T. Ravi G. Charulatha D.Y. Shahare Hamzeh J Aljawawdeh Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering college Chennai Department of ECE Rajalakshmi Engineering College Chennai Department of ECE Sathyabama Institute of Science and Technology Chennai Department of Biomedical Engineering Selvam College of Technology Namakkal Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Software Engineering faculty of information technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Entirely common sleep disorder is the dangerous wakefulness (Apnea) of breathing during snoring. In this study, we explore the possibility of how medical image processing can be useful and accurate method for sleep disordered breathing detection. The new approach named as Sleep Apnea Analyzing Strategy (SAAS) is firstly tested with the help of existing method namely Image base Sleep Apnea Analyzer (ISAA) that use less number or data to be processed and hence running on portable device delivering results much faster than other available methods, both being cross-validated against each other. Sleep Apnea Image-based Detector using Ephysiology - A new Algorithm (SAAS) We rely on sophisticated image processing techniques to extract relevant characteristics from medical images (such as x-rays, CT scans or MRIs) which are indicative of patterns typically associated with sleep apnea. The proposed methodology includes image preprocessing to improve the quality of images, feature extraction for quantification and characterization of all relevant information during OSA trials in GUI interface through well-known classification algorithms to classify patients with at least 20 de-saturations without sleep apnea. Our research aims to help in the creation of automatic diagnostic methods that will lead to early detection and treatment for sleep apnea. Our objective with this work is to provide a fair evaluation of the proposed technique in terms of sensitivity, specificity and accuracy through using a large dataset.

关键词： Accuracy Sensitivity Computational modeling Signal processing algorithms X-rays Signal processing Feature extraction Sleep apnea Biomedical image processing Medical diagnostic imaging

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GPT-Based Wasm Instruction Analysis for Program Language Processing 5th

GPT-Based Wasm Instruction Analysis for Program Language Pro...

引用

5th International Conference on Emerging information Security and Applications, EISA 2024

作者： Deng, Liangjun Zhong, Qi Lei, Hang Qiu, Yao Chen, Jingxue School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Faculty of Data Science City University of Macau China Ningbo Polytechnic College Zhejiang Ningbo315800 China School of Mathematical and Physical Sciences University of Technology Sydney SydneyNSW2007 Australia

ISBN: (纸本)9783031804182

Capturing the logical structure of programming languages poses a significant challenge for program analysis. Given the complex syntax rules, subjective code vulnerabilities, irrelevant statements, code annotations, and intricate structural information, related studies have explored various semantic comprehension and intermediate representation approaches to extract precise information for program analysis. However, most research in the generic domain ignores defective and non-defective program code, putting them in the same category. In this paper, we introduce a new program analysis method that combines WebAssembly (Wasm) instructions with a 20-billion-parameter transformer model and natural language processing. This approach aims to advance the capabilities of program analysis tools in computer science by jointly embedding Wasm instructions and natural language for more effective program analysis. Our experiments demonstrate that this fused embedding approach achieves state-of-the-art performance, and the accuracy reaches approximately 98 percent, better than traditional small-scale weight models based on intricate conversion tasks such as abstract syntax trees(AST). Moreover, it is more valuable to classify potential vulnerable and non-vulnerable programs in the formal verification special field. Our exploration enhances traditional program classification methods in software security and introduces the application of GPT to offer a more straightforward, convenient, and high-performance approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Formal verification

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Improving WiFi Fingerprint Database With Irregular Distribution of Reference Points Using Interpolation Methods

Improving WiFi Fingerprint Database With Irregular Distribut...

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International Conference on Advanced Technologies for Communications, ATC

作者： Trung Vu-Thanh Jing He Ninh Duong-Bao Seon-Woo Lee Luong Nguyen Thi Khanh Nguyen-Huu College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Mathematics and Informatics Dalat University Dalat Vietnam Division of Software Hallym University Chuncheon Korea Faculty of Information Technology Dalat University Dalat Vietnam Department of Electronics and Telecommunications Dalat University Dalat Vietnam

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

Using WiFi Fingerprinting for indoor positioning is becoming increasingly popular due to its cost-effectiveness. However, to ensure the system can operate with high accuracy and stability, this technique requires a burden of data collection tasks from a dense distribution of reference points (RPs). To decrease the cost of data collection, as well as to improve the WiFi fingerprint database with irregular distribution of RPs, this study applies and compares different interpolation methods. Evaluating these methods in a public dataset including multiple sub-datasets with irregular distributions of RPs, the results highlight the Linear Barycentric interpolation (LB) as the most favorable method due to its low complexity and stable performance. The LB-based fingerprint database achieves an average interpolation error of less than 3 dBm compared to the original one. When applying the interpolated database to positioning tasks, it can help reduce acquisition costs by up to 50% while improving positioning accuracy by up to 11%. The study reveals the feasibility of interpolation in optimizing WiFi fingerprint data collection, especially in large and complex environments where regular distribution of RPs cannot be deployed.

关键词： Interpolation Costs Accuracy Databases Fingerprint recognition Data collection Reliability Computational complexity Wireless fidelity Floors

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Are Eeg-to-Text Models Working?

SSRN

引用

SSRN 2024年

作者： Jo, Hyejeong Yang, Yiqian Han, Juhyeok Duan, Yiqun Xiong, Hui Lee, Won Hee Department of Software Convergence Kyung Hee University Gyeonggi-do Yongin17104 Korea Republic of Guangdong Guangzhou511453 China GrapheneX-UTS HAI Centre Australian Artificial Intelligence Institute Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia

This work critically analyzes existing models for open-vocabulary EEG-to-Text translation. We identify a crucial limitation: previous studies often employed implicit teacher-forcing during evaluation, artificially inflating performance metrics. Additionally, they lacked a critical benchmark – comparing model performance on pure noise inputs. We propose a methodology to differentiate between models that truly learn from EEG signals and those that simply memorize training data. Our analysis reveals that model performance on noise data can be comparable to that on EEG data. These findings highlight the need for stricter evaluation practices in EEG-to-Text research, emphasizing transparent reporting and rigorous benchmarking with noise inputs. This approach will lead to more reliable assessments of model capabilities and pave the way for robust EEG-to-Text communication systems. Code is available at https://***/NeuSpeech/EEG-To-Text. © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Model Checking Quantum Continuous-Time Markov Chains 32

Model Checking Quantum Continuous-Time Markov Chains

引用

32nd International Conference on Concurrency Theory, CONCUR 2021

作者： Xu, Ming Mei, Jingyi Guan, Ji Yu, Nengkun MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China State Key Lab of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9783959772037

Verifying quantum systems has attracted a lot of interests in the last decades. In this paper, we initialise the model checking of quantum continuous-time Markov chain (QCTMC). As a real-time system, we specify the temporal properties on QCTMC by signal temporal logic (STL). To effectively check the atomic propositions in STL, we develop a state-of-the-art real root isolation algorithm under Schanuel's conjecture;further, we check the general STL formula by interval operations with a bottom-up fashion, whose query complexity turns out to be linear in the size of the input formula by calling the real root isolation algorithm. A running example of an open quantum walk is provided to demonstrate our method. © Ming Xu, Jingyi Mei, Ji Guan, and Nengkun Yu;.

关键词： Model checking

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