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4th International Conference on Cybernetics and Intelligent system, ICORIS 2022

作者： Pramono, Andi Munir, Badrul Farchan, Muchammad Budiman, Satrio Arif Azis, Baskoro Putra, Wahyu Waskito Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia Brawijaya University Departemen of Neurology Malang Indonesia Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Electrical Engineering Surabaya Indonesia Brawijaya University Information System Computer Science Faculty Malang Indonesia

ISBN: (纸本)9781665453950

Everyone has a fundamental desire for sleep. By sleeping, humans can conserve energy and replenish their stamina to optimal levels. Lighting is one component that influences the quality of a person's sleep. One of them is a person who sleeps with the lights on, suffers from nyctophobia, has poor sleep quality, and may be responsible for several illnesses. This study aims to develop a smart lighting device that will aid in treating youngsters with nyctophobia to get used to sleeping with the lights off to improve their sleep quality. The case study methodology used in this study entails the development of a prototype. This method employs an oximeter as a heart rate detector and a sensor comparison as the measurement device. Arduino Uno circuit and heart rate sensor are the main devices in this research. At the same time, the NRF 24L01 is a communication medium between the two Arduino Uno boards. The experimental measurements in normal conditions indicate that the heart rate sensor was nearly the same as the value displayed by the oximeter. Similarly, after running for 1 minute, both devices produced almost identical results, with an average difference of 1 Beats Per Minute (BPM). According to experiments with a heart rate sensor, the switch-off condition can be activated if the heart rate hits 90 BPM. In this case, the light will switch off once the heart rate hits 70 BPM, which might be used for sleeping individuals. © 2022 IEEE.

关键词： Oximeters

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Efficient and Non-Invasive Rice Moisture Quantification Using Coupled Lines Microwave Sensor Design

Efficient and Non-Invasive Rice Moisture Quantification Usin...

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International Conference on Information technology (INCIT)

作者： Pratya Phanomupatum Ravee Phromloungsri Decha Wongpatsa Tiwat Pongthavornkamol Phatcharanat Saeng-On Somchat Sonasang Electronics Faculty of Industrial Technology Nakhon Phanom University Nakhon Phanom Thailand Computer and Communication Engineering Faculty of Technology Udon Thani Rajabhat University Udon Thani Thailand Faculty of Agriculture and Technology Nakhon Phanom University Nakhon Phanom Thailand Location and Automatic Identification System Research Team Location and Automatic NECTEC National Science and Technology Development Agency Pathum Thani Thailand Electrical Elements and Wireless Devices Unit (EEWDU) Faculty of Industrial Technology Nakhon Phanom University Nakhon Phanom Thailand

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

This research presents the design, simulation, and experimental validation of a novel microwave sensor for noninvasive moisture measurement in rice. The sensor employs coupled microstrip lines fabricated on an FR4 substrate, designed to operate at a center frequency of 900 MHz with a quarter-wavelength configuration. The Advanced Design system software was utilized for the simulation, where key parameters such as S 21 and phase response were analyzed across a frequency range of 0.1 to 3 GHz. The sensor was fabricated and tested using a Vector Network Analyzer, where the moisture content in rice samples was varied incrementally, and the sensor’s performance was evaluated based on the changes in transmission characteristics. The results demonstrated the sensor’s potential for accurate moisture detection in rice, showcasing its feasibility as a noninvasive measurement technique in agricultural applications. This study contributes to the development of efficient, real-time moisture measurement technologies in the food processing industry.

关键词： Microwave measurement Sensitivity Moisture measurement Moisture Network analyzers Microwave sensors Vectors Real-time systems system software Substrates

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Multimodal Dialogue Emotion Recognition Based on Label Optimization and Coarse-Grained Assisted Fine-Grained

Multimodal Dialogue Emotion Recognition Based on Label Optim...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant information from other utterances, reducing the accuracy of emotion recognition. Existing one-hot labels often fail to capture the full range of emotional expressions, leading to biased results. To address these issues, we propose a model that fuses different modalities within the same utterance to avoid redundancy. It employs a progressive classification process, refining emotion recognition from coarse to fine granularity. Additionally, we use emotion polarity probabilities as weights for fine-grained classification and introduce a multimodal information-rich label that considers both the data and their interactions. Experiments on IEMOCAP and MELD datasets demonstrate the model’s effectiveness, significantly improving dialog emotion recognition accuracy. Our code is available at https://***/r/LOCG-188E.

关键词： Emotion recognition Accuracy Fuses Refining Termination of employment Speech recognition Signal processing Speech processing Optimization Faces

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Digital forensics for the socio-cyber world (DF-SCW): A novel framework for deepfake multimedia investigation on social media platforms

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Egyptian Informatics Journal 2024年 27卷

作者： Ayub Khan, Abdullah Chen, Yen-Lin Hajjej, Fahima Ahmed Shaikh, Aftab Yang, Jing Soon Ku, Chin Yee Por, Lip Department of Computer Science and Information Technology Benazir Bhutto Shaheed University Lyari Sindh Karachi75660 Pakistan Department of Computer Science Sindh Madressatul Islam University Sindh Karachi74000 Pakistan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei106344 Taiwan Department of Software Engineering College of Computer and Inf. Sci. Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Department of Computer Science Universiti Tunku Abdul Rahman Kampar31900 Malaysia

Owing to the major development of social media platforms, the usage of technological adaptation increases by means of editing software tools. Posting media in social communication environments has become one of our common daily routines. Before posting, various editing generators are used to manipulate pixel values, such as for enhancing brightness and contrast. Undoubtedly, this software helps bring posting media from ordinary to outstanding. But such a type of editing crosses the line in terms of creating fakes—anything that comes from anywhere and does not retain its originality anyway. It poses a series of issues in the process of multimedia forensics investigation and chain of custody. In order to restrict the attempts at deep faking and make the investigation hierarchy more effective, efficient, and reliable in the socio-cyber space (SCS), this paper presents a novel framework called DF-SCW. A digital forensics-enabled socio-cyber world with artificial intelligence (AI), especially deep neural networks (DNNs), for detecting and analyzing deep fake media investigations on social media platforms. It compares pixels with their neighboring values in the same media (such as images and videos) to identify information about the original one. There is a media flag designed to filter out malicious and dangerous attempts, like a powerful leader declaring war. Putting flags on such fakes helps digital investigators resist sharing the posts. In addition, the other prospect of this research is to make the digital forensics ecosystem more appropriate to take qualitative judgments in real-time while media is uploaded on social media platforms. The simulation of the proposed DF-SCW is tested on three different platforms, such as Instagram, Facebook, and Twitter. Through the experiment, the DF-SCW outperformed in terms of detection, identification, and analysis of deepfake media by an increased rate of 3.77%. © 2024

关键词： Social networking (online)

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Deep Learning Techniques for Advanced Large-Scale Data Processing

Deep Learning Techniques for Advanced Large-Scale Data Proce...

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International Conference on Computing and Networking technology (ICCNT)

作者： Mp Sunil Jyoti Shekhawat T. Vanaja Vazeer Ali Mohammed Shailesh J. Thaware Vaishali Singh Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer & Information System Lewis University Illinois USA Department of Computer Engineering Vishwakarma Institute of Technology Pune INDIA Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Deep getting to know techniques have become increasingly more popular due to their potential to improve huge-scale records processing performance. Leveraging specialized hardware which includes GPUs, deep getting to know algorithms can attain mind-blowing pace will increase compared to traditional algorithms. further, deep gaining knowledge of fashions can discover complicated non-linear relationships in big datasets, permitting greater accurate overall performance and less fake predictions. Latest advances in deep mastering have enabled it to be implemented to remedy a plethora of information processing duties, from photo recognition to natural language processing. CNNs, RNNs, and GANs are a number of the maximum famous deep gaining knowledge of fashions used in tackling complex data processing obligations. agencies consisting of Google, fb, and Amazon use these sophisticated frameworks to build predictive models that help improve numerous programs. Deep getting to know strategies offer exquisite ability to take massive-scale information processing to the following stage. corporations are increasingly more becoming privy to the emerging opportunities, and actively investing in deep mastering to liberate the untapped strength of their information.

关键词： Deep learning Accuracy Scalability Information processing Predictive models Data processing Prediction algorithms Hardware Natural language processing Optimization

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A Hybrid Sine Cosine Algorithm with Cluster Voting for Combinatorial Testing

A Hybrid Sine Cosine Algorithm with Cluster Voting for Combi...

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Cyber Security and Computing (CyberComp), International Conference on

作者： Masrulehsan Mamat Mazidah Mat Rejab Kamal Z. Zamli Ashok Sivaji Faculty of Computer Science and Information System Universiti Tun Hussein Onn Malaysia (UTHM) Parit Raja Malaysia Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang MIMOS Berhad MRANTI Technology Park Bukit Jalil Kuala Lumpur

ISBN: (数字)9798350387728

ISBN: (纸本)9798350387735

Combinatorial testing is essential for detecting system faults with minimal test cases. However, traditional methods often struggle to efficiently explore large search spaces, leading to redundant test cases and increased computational costs. This paper introduces a novel hybrid optimization framework, HCV-SCA, which integrates the Sine Cosine Algorithm (SCA), JAYA, and Teaching-Learning-Based Optimization (TLBO) with a dynamic Cluster Voting (CV) mechanism. The CV mechanism dynamically selects and adjusts search operators, penalizing or rewarding them based on their performance, ensuring the production of optimal test suites. Unlike random or exhaustive methods, metaheuristic algorithms like HCV-SCA intelligently navigate complex search spaces, generating test suites with improved coverage and reduced redundancy. Experimental evaluations using benchmark combinatorial testing problems demonstrate that HCV-SCA outperforms existing methods, particularly excelling in smaller test suite configurations (t = 2 to 7) and maintaining competitive performance for larger configurations (t = 8 to 10). The results show a significant reduction in test suite sizes, enhanced fault detection rates, and improved computational efficiency. The adaptive nature of the Cluster Voting mechanism ensures robustness across diverse testing scenarios, making HCV-SCA a powerful and flexible solution for optimizing combinatorial testing. This research sets a new benchmark for effective test case generation in software testing, addressing both efficiency and adaptability in complex systems.

关键词： Combinatorial testing Heuristic algorithms Fault detection Software algorithms Metaheuristics Clustering algorithms Benchmark testing Robustness Computational efficiency Space exploration

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Insights Into the Future: XAI Integration in O-RAN and Space Communication systems

Insights Into the Future: XAI Integration in O-RAN and Space...

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Multimedia University Engineering Conference (MECON)

作者： H. Ahmed Tahir Fahad Nabi Muhammad Ziad Tariq Ahmad Fasih Khan Azwan Mahmud Faculty of Engineering Multimedia University Cyberjaya Malaysia Department of Electrical Engineering & Information Technology University of Kiel Kiel Germany Department of Computer Science SZABIST University Islamabad Pakistan Roots School System Islamabad Pakistan

ISBN: (数字)9798331530747

ISBN: (纸本)9798331530754

This paper outlines the merging of Explainable Artificial Intelligence (XAI) with Open Radio Access Network (O-RAN) and space communication systems. The usage of AI drives decision-making in critical domains of telecommunications and space explorations requires transparency and interpretability. However, traditional AI models often operate as “black boxes,” making it difficult to understand their decision-making processes. This lack of explainability poses significant risks, including misinterpretation of signals, undetected anomalies, and erroneous decision-making, which can compromise the integrity of communication systems. Specifically, the challenges include accurately distinguishing between legitimate signals and attacks in anti-jamming scenarios, understanding the behaviour of complex models like LSTM in traffic prediction, and ensuring the reliability of telemetry data despite errors and noise. Integration of XAI techniques within O-RAN architecture and space communication protocols can ensure trust, reliability and safety in AI-enabled systems. This paper investigates the opportunities and challenges of incorporating XAI in O-RAN and space communications. Additionally, it presents the implications of XAI, the need for interpretability in autonomous spacecraft operations, anomaly detection and decision support for mission-critical tasks. By bridging the gap in AI transparency and advanced communication technology, this paper aims to present a detailed analysis of implemented AI and Machine Learning (ML) in the telecommunication domain. It also presents the challenges currently faced in the intersection of XAI and communication. Use cases of XAI in the domain of Open Radio Access Network and Space Communications in light of LIME and SHAP techniques were presented to give a hypothetical modelling for future experimentation to provide local and global interpretation and explanations for the currently employed AI/ML models in the respective domains. Overall, this

关键词： Surveys Space vehicles Explainable AI Space communications Decision making Open RAN Reliability Telemetry Artificial intelligence Virtualization

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The object detection model uses combined extraction with KNN and RF classification

arXiv

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arXiv 2024年

作者： Kurniati, Florentina Tatrin Manongga, Daniel H.F. Sembiring, Irwan Wijono, Sutarto Huizen, Roy Rudolf Faculty of Information Technology Universitas Kristen Satya Wacana Salatiga Indonesia Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Indonesia Faculty of Psychology Universitas Kristen satya Wacana Salatiga Indonesia Department of Magister Information System Institut Teknologi dan Bisnis STIKOM Bali Indonesia

Object detection plays an important role in various fields. Developing detection models for 2D objects that experience rotation and texture variations is a challenge. In this research, the initial stage of the proposed model integrates the gray-level co-occurrence matrix (GLCM) and local binary patterns (LBP) texture feature extraction to obtain feature vectors. The next stage is classifying features using k-nearest neighbors (KNN) and random forest (RF), as well as voting ensemble (VE). system testing used a dataset of 4,437 2D images, the results for KNN accuracy were 92.7% and F1-score 92.5%, while RF performance was lower. Although GLCM features improve performance on both algorithms, KNN is more consistent. The VE approach provides the best performance with an accuracy of 93.9% and an F1-score of 93.8%, this shows the effectiveness of the ensemble technique in increasing object detection accuracy. This study contributes to the field of object detection with a new approach combining GLCM and LBP as feature vectors as well as VE for classification. © 2024, CC BY.

关键词： Local binary pattern

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Gender Identification Using Marginalised Stacked Denoising Autoencoders on Twitter Data

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Intelligent Automation & Soft Computing 2023年第6期36卷 2529-2544页

作者： Badriyya B.Al-onazi Mohamed K.Nour Hassan Alshamrani Mesfer Al Duhayyim Heba Mohsen Amgad Atta Abdelmageed Gouse Pasha Mohammed Abu Sarwar Zamani Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Teachers Training Arabic Linguistics InstituteKing Saud UniversityP.O.BOX 145111RiyadhZIP 4545Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16733Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Gender analysis of Twitter could reveal significant socio-cultural differ-ences between female and male *** had been made to analyze and auto-matically infer gender formerly for more commonly spoken languages’content,but,as we now know that limited work is being undertaken for *** of the research works are done mainly for English and least amount of effort for non-English *** study for Arabic demographic inference like gen-der is relatively uncommon for social networking users,especially for ***,this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification on Arabic Twitter(OMSDAE-GIAT)*** presented OMSDAE-GIAR technique mainly concentrates on the identifica-tion and classification of gender exist in the Twitter *** attain this,the OMS-DAE-GIAT model derives initial stages of data pre-processing and word ***,the MSDAE model is exploited for the identification of gender into two classes namely male and *** the final stage,the OMSDAE-GIAT technique uses enhanced bat optimization algorithm(EBOA)for parameter tuning process,showing the novelty of our *** performance validation of the OMSDAE-GIAT model is inspected against an Arabic corpus dataset and the results are measured under distinct *** comparison study reported the enhanced performance of the OMSDAE-GIAT model over other recent approaches.

关键词： Arabic twitter gender identification bat algorithm hybrid deep learning social media arabic corpus

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From Competition to Classroom: A Hands-on Approach to Robotics Learning 25

From Competition to Classroom: A Hands-on Approach to Roboti...

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25th IEEE International Conference on Autonomous Robot systems and Competitions, ICARSC 2025

作者： Lopes, Maria S. Ribeiro, Jorge Diogo Moreira, Antonio Paulo Rocha, Claudia D. Martins, João G. Sarmento, Jose Maria Carvalho, Jose Pedro Costa, Paulo G. Sousa, Ricardo B. University of Porto (FEUP) Faculty of Engineering Porto 4200-465 Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto 4200-465 Portugal University of Tras-os-Montes and Alto Douro (UTAD) Vila Real 5000-801 Portugal SYSTEC-ARISE Research Center for System and Technologies Porto 4200-465 Portugal

ISBN: (纸本)9798331538606

Robotics education plays a crucial role in developing STEM skills. However, university-level courses often emphasize theoretical learning, which can lead to decreased student engagement and motivation. In this paper, we tackle the challenge of providing hands-on robotics experience in higher education by adapting a mobile robot originally designed for competitions to be used in laboratory classes. Our approach integrates real-world robot operation into coursework, bridging the gap between simulation and physical implementation while maintaining accessibility. The robot's software is developed using ROS, and its effectiveness is assessed through student surveys. The results indicate that the platform increases student engagement and interest in robotics topics. Furthermore, feedback from teachers is also collected and confirmed that the platform boosts students' confidence and understanding of robotics. © 2025 IEEE.

关键词： Educational robotics Hands-on learning Mobile robots Robotic competitions STEM

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