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An automated stress detection model based on dual approach of clinical psychologist prediction and machine learning

International Journal of Information technology (Singapore) 2025年第2期17卷 755-765页

作者： Narzary, Diptimoni Sharma, Uzzal Khanna, Ashish Department of Computer Applications Assam Don Bosco University Guwahati India Department of Computer Science Birangana Sati Sadhani Rajyik Viswavidyalaya Golaghat India Centre for Global Health Research AI & amp Health Unit Saveetha Medical College Saveetha Institute of Medical and Technical Sciences Chennai India Maharaja Agrasen Institute of Technology GGSIPU Delhi India

The timely detection of stress has become increasingly difficult because of the growing number of stressed individuals and the lack of availability of diagnostic tools. Clinical psychology mainly relies on manual detection methods, which, although effective, depend on the experience and expertise of the doctor. To address this issue, the proposed research is undertaken where a system is proposed to be developed to assess stress levels using speech samples of the target individual. This study aimed to identify stress levels from recorded audio based on acoustic characteristics. Data from 800 voice recordings of 100 participants at the Lakshmibai National Institute of Physical Education (LNIPE), North East Regional Center (NERC) Guwahati, were collected based on responses to eight questions prepared in consultation with the clinical psychiatrist. This is done so that, while answering the questions by any participant, the state of the mind of that individual is expressed. The model employs Mel Frequency Cepstral Coefficients (MFCC) and Linear Prediction Cepstral Coefficients (LPCC) with a Linear Support Vector Machine (SVM) classifier using the binary kernel, achieving an accuracy of 88%, marking a promising step towards automatic detection of stress. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Clinical psychiatrist LPCC Machine learning Mental state MFCC Stress SVM

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DITS: A New Domain Independent Text Spotter 27th

DITS: A New Domain Independent Text Spotter

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Purkayastha, Kunal Sarkar, Shashwat Shivakumara, Palaiahnakote Pal, Umapada Ghosal, Palash Wu, Xiao-Jun Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Baranagar India School of Science Engineering and Environment University of Salford Manchester United Kingdom Department of Information Technology Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim Gangtok India Jiangnan University Wuxi China

ISBN: (纸本)9783031784941

Text spotting in diverse domains, such as drone-captured images, underwater scenes, and natural scene images, presents unique challenges due to variations in image quality, contrast, text appearance, background complexity, and external factors like water surface reflections and weather conditions. While most existing approaches focus on text spotting in natural scene images, we propose a Domain-Independent Text Spotter (DITS) that effectively handles multiple domains. We innovatively combine the Real-ESRGAN, developed for regular image enhancement, with the DeepSolo, developed for scene text spotting, in an end-to-end fashion for text detection and spotting on images of different domains. The key idea behind our approach is that improving image quality and text-spotting accuracy are complementary goals. Real-ESRGAN enhances image quality, making the text more discernible, while DeepSolo, a state-of-the-art text spotting model, accurately localizes and recognizes text in the enhanced images. We validate the superiority of our proposed model by evaluating it on datasets from drone, underwater, and scene domains (ICDAR 2015, CTW1500, and Total-Text). Furthermore, we demonstrate the domain independence of our model through cross-domain validation, where we train on one domain and test on others. Our dataset and code will be publicly available on GitHub. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image quality

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Suggesting Exit Path in a Multi-Path in Case of an Emergency 18

Suggesting Exit Path in a Multi-Path in Case of an Emergency

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Vignesh, M.S. Kumar, Nikhil Mukhopadhyay, Adwitiya Rani, Pooja Kumar, Manoj Joshi, Pallavi Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India Bml Munjal University School of Engineering and Technology Gurugram India Vspp Tech Llc TX United States University of Wollongong in Dubai School of Computer Science Feis Dubai United Arab Emirates Middle East University Meu Research Unit Amman Jordan

ISBN: (纸本)9789380544519

This study proposes an innovative approach to address the challenges of navigating multi-exit scenarios during emergencies by utilizing the VGG19 model and incorporating voice guidance. A crucial component of this research is the extensive CubiCasa5K dataset, comprising 5,000-floor plans with detailed annotations, including exit paths and structural information from diverse indoor environments. These floor plans serve as a valuable resource for generating synthetic images of exit paths within various buildings. The synthetic images undergo pre-processing and are inputted into the VGG19 model for feature extraction and exit path classification. To enhance user experience, a text-to-speech synthesis system is integrated, generating a tailored voice message offering clear and concise instructions based on the VGG19's classification of the exit path. The solution achieves an impressive accuracy of 94.96%, promising to significantly improve individual safety by providing accurate and timely guidance in complex emergency situations. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： accuracy CubiCasa5K Dataset deep learning floor plans VGG19 voice guidance

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Research on defect detection of photovoltaic cells based on improved mask R-CNN-attention model

Research on defect detection of photovoltaic cells based on ...

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2024 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2024

作者： Yang, Guang Zhang, Jun Li, Haitao Bai, Chenbo Changqing Engineering Design Co. Ltd. Changqing Oilfield Changqing China Science and Technology Development Department Changqing Oilfield Changqing China Digital and Intelligent Business Unit Changqing Oilfield Changqing China School of Computer Science Xi'an Shiyou University Xi'an China

ISBN: (纸本)9781510682597

Photovoltaic cells play a crucial role in the field of solar power generation. However, they are often affected by defects in photovoltaic panels, which directly harm their performance. In order to solve this problem, this study proposes an improved Mask R-CNN-Attention model for defect detection of photovoltaic cell components. By introducing attention mechanism, the model can focus more accurately on key image areas, and adopts a multi-scale feature pyramid optimization to improve recognition accuracy. The experiment proves that this method has achieved significant improvement in the defect detection of photovoltaic cell components, providing an effective technical means for solving this problem. This innovative method is expected to improve the performance and maintenance efficiency of photovoltaic cell components, bring positive impact to the solar power generation industry, and provide key support for the sustainable performance and maintenance management of photovoltaic cell components. © 2024 SPIE.

关键词： Solar cells Object detection Defect detection Performance modeling Feature extraction Semantics Education and training Photovoltaics Target detection Visualization

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Inflated 3D ConvNet for detection of sign language 4

Inflated 3D ConvNet for detection of sign language

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4th International Conference on Signal Processing and Machine Learning, CONF-SPML 2024

作者： Chandana, D. Tushara, M. Sri, A. Ramya Sakhamuri, Sridevi Abualigah, Laith Department of Electronics and Computer Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram522502 India Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq25113 Jordan MEU Research Unit Middle East University Amman Jordan

ISBN: (纸本)9781510674721

Every human has their own kind of disabilities, we all try to live and overcome them in our life. We educate ourselves to overcome them, we invent technology to achieve our goals. Sign Language is a communication path for deaf-mute people through hand gestures and actions. Sign Language helps people who can’t speak sign language to interact with the people who can speak sign language, this deep learning paper aims to help build a communication bridge for this reason. We used Amazon Rekognition service which uses Deep CNN algorithm for the detection of static images of the signs. As most of the signs are for words they are in the form of videos. We used the I3D algorithm for the classification of videos of the signs of words. The PyTorch framework provides support for CuDNN (NVIDIA CUDA Deep Neural Network) which provides fast GPU implementations for the deep neural networks. The Experimental results has shown that the models used has displayed good results in detecting the words. © 2024 SPIE.

关键词： Deep neural networks

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"Fiction to Function"Shaping Renewable Energy Education with MATLAB and ChatGPT-Driven Environments 21

"Fiction to Function"Shaping Renewable Energy Education with...

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21st International Learning and technology Conference, L and T 2024

作者： Mohamed, Baher S., Babiker A., Aldybous N., Kabbaj T., Brahimi Effat University Effat College of Engineering Electrical and Computer Engineering Department Jeddah Saudi Arabia Effat University Effat College of Engineering Energy and Technology Research Center Electrical and Computer Engineering Department Jeddah Saudi Arabia Effat University Natural Science Math and Tech Unit Effat College of Engineering Energy and Technology Research Center Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Addressing the gap in effective and engaging renewable energy education, this study, 'Fiction to Function,' transitions imaginative frameworks into practical educational tools, focusing on solar and wind energy in line with Sustainable Development Goals (SDGs). The goal is to empower users with deep insights into renewable energy through the novel integration of MATLAB and ChatGPT-driven environments, enhancing the analysis and exploration of these energy sources. The study employs a structured approach using MATLAB App Designer to develop a user-friendly GUI, aligning instructional content with the SDGs and integrating ChatGPT via the OpenAI API for dynamic, interactive learning experiences. This integration provides a multifaceted platform that not only educates but also engages users in practical scenarios, bridging the gap between theoretical knowledge and real-world application. The results demonstrate an enhanced understanding of renewable energy concepts among users, showcasing the efficacy of combining MATLAB's analytical capabilities with ChatGPT's AI-driven guidance. Ultimately, the study highlights the transformative potential of innovative technology in sustainable energy education, setting a precedent for future educational tools in this domain. © 2024 IEEE.

关键词： Integration

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Collective Learning Mechanism based Optimal Transport Generative Adversarial Network for Non-parallel Voice Conversion

arXiv

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arXiv 2025年

作者： Dhar, Sandipan Akhter, Md. Tousin Jana, Nanda Dulal Das, Swagatam Department of Computer Science and Engineering National Institute of Technology Durgapur India Department of Computer Science and Engineering Indian Institute of Technology Bombay India Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata India

After demonstrating significant success in image synthesis, Generative Adversarial Network (GAN) models have likewise made significant progress in the field of speech synthesis, leveraging their capacity to adapt the precise distribution of target data through adversarial learning processes. Notably, in the realm of State-Of-The-Art (SOTA) GAN-based Voice Conversion (VC) models, there exists a substantial disparity in naturalness between real and GAN-generated speech samples. Furthermore, while many GAN models currently operate on a single generator discriminator learning approach, optimizing target data distribution is more effectively achievable through a single generator multi-discriminator learning scheme. Hence, this study introduces a novel GAN model named Collective Learning Mechanism-based Optimal Transport GAN (CLOT-GAN) model, incorporating multiple discriminators, including the Deep Convolutional Neural Network (DCNN) model, Vision Transformer (ViT), and conformer. The objective of integrating various discriminators lies in their ability to comprehend the formant distribution of mel-spectrograms, facilitated by a collective learning mechanism. Simultaneously, the inclusion of Optimal Transport (OT) loss aims to precisely bridge the gap between the source and target data distribution, employing the principles of OT theory. The experimental validation on VCC 2018, VCTK, and CMU-Arctic datasets confirms that the CLOT-GAN-VC model outperforms existing VC models in objective and subjective assessments. © 2025, CC BY.

关键词： Generative adversarial networks

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Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

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Neural Computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

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Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment

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computers, Materials & Continua 2023年第2期74卷 3101-3115页

作者： Mohammed Maray Hamed Alqahtani Saud S.Alotaibi Fatma S.Alrayes Nuha Alshuqayran Mrim M.Alnfiai Amal S.Mehanna Mesfer Al Duhayyim Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of information systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

Cloud Computing(CC)is the most promising and advanced technology to store data and offer online services in an effective *** such fast evolving technologies are used in the protection of computerbased systems from cyberattacks,it brings several advantages compared to conventional data protection *** of the computer-based systems that effectively protect the data include Cyber-Physical Systems(CPS),Internet of Things(IoT),mobile devices,desktop and laptop computer,and critical *** software(malware)is nothing but a type of software that targets the computer-based systems so as to launch cyberattacks and threaten the integrity,secrecy,and accessibility of the *** current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification(OBDDBNCMC)*** presentedOBDDBN-CMCmodel intends to recognize and classify the malware that exists in IoT-based cloud *** attain this,Zscore data normalization is utilized to scale the data into a uniform *** addition,BDDBN model is also exploited for recognition and categorization of *** effectually fine-tune the hyperparameters related to BDDBN model,GrasshopperOptimizationAlgorithm(GOA)is *** scenario enhances the classification results and also shows the novelty of current *** experimental analysis was conducted upon OBDDBN-CMC model for validation and the results confirmed the enhanced performance ofOBDDBNCMC model over recent approaches.

关键词： Malware detection security Internet of Things cloud computing machine learning parameter adjustment

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Boosting white shark optimizer for global optimization and cloud scheduling problem

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Neural Computing and Applications 2024年第18期36卷 10853-10879页

作者： Mostafa, Reham R. Chhabra, Amit Khedr, Ahmed M. Hashim, Fatma A. University of Sharjah Sharjah27272 United Arab Emirates Information Systems Department Faculty of Computers and Information Sciences Mansoura University Mansoura35516 Egypt Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar143005 India Computer Science Department University of Sharjah Sharjah27272 United Arab Emirates Faculty of Engineering Helwan University Helwan Egypt MEU Research Unit Middle East University Amman11831 Jordan

With the growing adoption of cloud computing in both public and private sector enterprises, the industry has experienced rapid expansion. To fully unlock the potential of cloud computing, efficient task scheduling becomes crucial. In cloud computing, task scheduling involves optimizing the allocation of tasks to a diverse range of resources, such as virtual machines, with the goals of reducing makespan, maximizing resource utilization, and minimizing response times. This challenge becomes even more pronounced for large-scale tasks due to the NP-hard nature of the problem. Consequently, the integration of metaheuristic algorithms into task scheduling has emerged as a solution to equitably distribute complex and diverse tasks across limited resources within acceptable timeframes. To enhance the quality of cloud computing services, this research introduces the modified white shark optimizer (mWSO) as an alternative task scheduling technique. The improved variant mWSO boosts the performance of the original WSO by introducing the following three enhancement steps: (1) introduce memory-based WSO to boost the exploitation phase, (2) propose an exploration-exploitation balance phase to enhance the exploration phase, and (3) introduce a control randomization parameter to balance exploration and exploitation properly. The mWSO is subjected to testing on both the global optimization problems from CEC2020 and cloud task scheduling problems. The experimental results of mWSO demonstrate high performance for CEC2020 competition benchmarks compared to other state-of-the-art and recent metaheuristic algorithms. In the case of the task scheduling problem, the mWSO achieved − 0.01 to 13.53% and 0.62–10.42% makespan and energy consumption reduction, respectively, for CEA-Curie workloads. For HPC2N workloads, mWSO achieved 7.27–29.53% makespan reduction and 3.52–26.08% energy savings over the compared metaheuristics. The statistical validity of the performance is also verified using Wil

关键词： Global optimization

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