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22nd IEEE International Conference on Industrial informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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A Multi-Modal Behavior Quantitative Analysis Model for Autism Early Screening

A Multi-Modal Behavior Quantitative Analysis Model for Autis...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Lei, Jiayi Zhang, E. She, Yingying Wang, Xin Liao, Yuhan Hu, Bin Wu, Hang Yang, Minqiang Tian, Jiajia Wang, Yong School of Informatics Xiamen University China University of Kansas Medical Center Department of Occupational Therapy Education United States School of Film National Institute for Data Science in Health and Medicine Xiamen University China School of Computer Science and Technology Beijing Institute of Technology China Together Education Institute China School of Information Science and Engineering Lanzhou University China Fujian Medical University Union Hospital Pediatric Department China

ISBN: (纸本)9798350337020

Human-computer Interaction (HCI) and Machine Learning (ML) technologies have potential for the behavioral screening of autistic children but how to design a tool and analyse behavior reliably is challenging. Based on psychophysiological computation, this paper proposes an interactive behavior perception analytical model for autism screening. We presented the multi-scenario reactive behavior paradigms that designed based on the atypical characteristics of autistic children. We recorded the eye movement data and facial data of 91 participants, and performed multi-modal feature extraction, used machine learning to train classification model. We conducted comparative experiments, and the experimental results verified the advantages of multi-scenario paradigms and multi-modal feature groups, which indicates that our analysis methods and screening models are effective and reliable and have real research significance. © 2023 IEEE.

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Sim2Real Grasp Pose Estimation for Adaptive Robotic Applications

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IFAC-PapersOnLine 2023年第2期56卷 5233-5239页

作者： Dániel Horváth Kristóf Bocsi Gábor Erdős Zoltán Istenes Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Eőtvős Loránd Research Network Budapest Hungary CoLocation Center for Academic and Industrial Cooperation Eőtvős Loránd University Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

Adaptive robotics plays an essential role in achieving truly co-creative cyber physical systems. In robotic manipulation tasks, one of the biggest challenges is to estimate the pose of given workpieces. Even though the recent deep-learning-based models show promising results, they require an immense dataset for training. In this paper, two vision-based, multi-object grasp pose estimation models (MOGPE), the MOGPE Real-Time and the MOGPE High-Precision are proposed. Furthermore, a sim2real method based on domain randomization to diminish the reality gap and overcome the data shortage. Our methods yielded an 80% and a 96.67% success rate in a real-world robotic pick-and-place experiment, with the MOGPE Real-Time and the MOGPE High-Precision model respectively. Our framework provides an industrial tool for fast data generation and model training and requires minimal domain-specific data.

关键词： adaptive robotics robot vision sim2real knowledge transfer smart manufacturing cyber physical production systems

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Distributed private preserving learning based chaotic encryption framework for cognitive healthcare IoT systems

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informatics in Medicine Unlocked 2024年 49卷

作者： Nabi, Shaik Abdul Kalpana, Ponugoti Chandra, N. Subhash Smitha, L. Naresh, K. Ezugwu, Absalom E. Abualigah, Laith Department of Computer Science and Engineering AVN Institute of Engineering and Technology Department of Computer Science and Engineering CVR College of Engineering Department of Information Technology G Narayanamma Institute of Technology and Science Department of Computer Science and Engineering TKR College of Engineering & Technology Unit for Data Science and Computing North-West University Computer Science Department Al Al-Bayt University MEU Research Unit Middle East University Applied Science Research Center Applied Science Private University Jadara Research Center Jadara University Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk School of Engineering and Technology Sunway University Malaysia

In the field of cognitive healthcare Internet of Things (CH-IoT), there is a strong demand for reliable and minimally intrusive smart gadgets that consistently acquire, analyse, and obtain the confidential health details of the individual. In fact, CH-IoT is empowered with artificial intelligence (AI) to transmute a fewer operational inputs into actionable, intelligent actions through the digitization of medical healthcare data. However, these systems consume more network complexity, interaction, and overhead costs, while inducing a blend of susceptibility and confidentiality issues. In support of this complexity, these cognitive systems need centralised data collection and to be gathered and analysed, which affects scalability issues and adds fuel to privacy and security breaches. Even though it possesses greater intricacy in its potential application, a substantial factor is maintaining the private preservation of healthcare data against the growing attacks. Thus, this paper presents a distributed privacy-preserving, chaotic encryption-based framework that can be deployed for CH-IoT systems to safeguard sensitive data against message modification, denial of service (DoS), and man-in-the-middle attacks (MIM), guaranteeing privacy and data integrity. The proposed framework integrated the federated learning layered hybrid chaotic encryption strategies by investigating through examination the learning infrastructure of convolutional neural networks (CNN). In the examination, the complete framework was carried out in the Tensorflow Federated Learning Libraries (FLL), and numerous performance metrics such as accuracy, precision, recall, f1-score, transmission efficiency, and overhead ratio were measured and contrasted with the various existing frameworks. For the intensive analysis, formal and informal security experiments were also conducted by NIST (National institute of Science and technology). The analytical results illustrate the importance of the proposed framewor

关键词： Cognitive health care Distributed learning Federated learning libraries Internet of things NIST Privacy and security breaches

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The Role of Bio informatics in Identifying Cancer informatics 2

The Role of Bio Informatics in Identifying Cancer Informatic...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Gole, Harshada Daronde, Subodh Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Deparment of Computer Science and Medical Engineering Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Biomedical Enginering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

With the advent of bioinformatics, cancer research has entered a new age wherein vast amounts of complex biological data can be interpreted to create powerful treatments. This approach has exceptionally improved the identification of biomarkers, deciphering hidden patterns contained within data, and developing personalized treatment modalities by incorporating bioinformatics-based strategies with artificial intelligence and machine learning. Advances in bioinformatics will soon give insight into the cancer process at cellular levels and offer a better understanding of tumor heterogeneity and evolution. Increased reliance on bioinformatics has directly affected the development of newer immunotherapy forms. In the last year, there have been 13 new FDA-approved immunotherapy treatments for 10 different drugs and 11 types of cancers specific to each one. The future of bioinformatics in cancer research is bright, and the field is likely to be further revolutionized with the continued progress in artificial intelligence, machine learning, and single-cell analysis techniques © 2025 IEEE.

关键词： Bioinformatics

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Sensor Based Smart Wearable Devices for Women: A Review 2

Sensor Based Smart Wearable Devices for Women: A Review

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Khadse, Chinmaya Hajare, Shital Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Natural organic menstrual cycles stay in large part taboo during many regions of India mainly rural areas hence threatening teenagers with useless menstrual hygiene requirements mixed with primary sanitation issues and network disapproval. The fitness problems and psychological misery get up from these subjects. The World Health Organization (WHO) establishes that inadequate menstrual hygiene ends in urinary tract infections and infections of the reproductive device. The importance of India's menstrual population now stands at 355 million girls and ladies which needs immediately reaction through powerful answers. This observe investigates how IA and IoT-driven smart wearable generation can increase menstrual health control (MHM) for teenage Indian women. A proposed low cost wearable tool collects basal body temperature (BBT) and heart charge variability (HRV) information and bodily activity information while retaining cultural appropriateness. AI chatbots and academic guide and actual-time comments capabilities exist inside this device. The system features complete menstrual health tracking competencies with the aid of using cycle prediction competencies and implements in-tool heating structures for ache discount together with strain reduction software for emotional aid. The system uses techniques that start from person desires even as imposing LSTM networks together with Random Forest Regression machines for forecasting menstrual cycles and symptom reviews. Validations run on the tool arise in practical conditions which observe both its performance accuracy along person pleasure quotes and reliability through the years. The observe shows wearable era has the capacity to revolutionize menstruation take care of Indian teens via presenting them with a powerful approach to address their public health needs. © 2025 IEEE.

关键词： mHealth

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Retraction Note: Resnet-based deep learning multilayer fault detection model-based fault diagnosis

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Multimedia Tools and Applications 2024年第38期83卷 86323-86324页

作者： Jaber, Mustafa Musa Ali, Mohammed Hasan Abd, Sura Khalil Jassim, Mustafa Mohammed Alkhayyat, Ahmed Majid, Mohammed Sh. Alkhuwaylidee, Ahmed Rashid Alyousif, Shahad Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Department of Computer Science Al-Turath University College Baghdad Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Najaf Iraq Directorate of Research and development Ministry of Higher Education and Scientific Research Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq College of Technical Engineering The Islamic University Najaf Iraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Computer Technical Engineering Mazaya University College Thi Qar Iraq Department of Electrical and Electronic Engineering College of Engineering Gulf University Sanad Kingdom of Bahrain Research Centre University of Mashreq Baghdad Iraq

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Applications of Machine Learning Techniques in Detecting Skin Cancer 5

Applications of Machine Learning Techniques in Detecting Ski...

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5th International Conference on Contemporary Computing and informatics, IC3I 2022

作者： Kunekar, Pankaj Vaishnav, Rounak Kalaivani, E. Gangodkar, Durgaprasad Kaur, Chamandeep Dhanraj, Joshuva Arockia Vishwakarma Institute of Technology Pune India Mahatma Gandhi University of Medical Sciences and Technology Clinical Research Manager Department of Medical Oncology Sri Ram Cancer and Super Specialty Centre Jaipur India Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Jazan University Department of Computer Science & Information Technology Saudi Arabia Department of Mechatronics Engineering Padur Chennai India

ISBN: (纸本)9798350398267

The most lethal kind of skin cancer is called malignant melanoma. Accurate diagnosis of skin malignancies are aided by dermoscopy, a non-invasive high-resolution imaging procedure. Melanoma is a very dangerous kind of skin cancer because of how quickly it spreads and how aggressive it is. For this reason, malignant melanoma maintains its place as one of the tumors with the highest rates of growth across the globe. Second-order texture feature data may be obtained by the GLCM (Grey Level Co-occurrence Matrix) technique. The reference pixel and neighboring pixels are considered by GLCM to ensure an accurate calculation. Grayscale pictures have a decent number of levels, which is straightforwardly relative to the number of segments and columns in the frameworks used to characterize the scale. Relative recurrence is addressed by the grid component P I j | Dx, Dy), where I and j are force values and Dx and Dy are pixel distances. In total, the co-occurrence matrix considers 14 different attributes, including energy, efficiency, latency, correlation, homogeneity, and contrast. Convolutional Neural networks (CNN) and Gaussian Filters (GF) classifiers were then used to sort these vectors. Comparisons of the results these classifiers provide are also made. © 2022 IEEE.

关键词： Pixels

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Evaluating the Inclusiveness of Artificial Intelligence Software in Enhancing Project Management Efficiency – A review and examples of quantitative measurement methods

Evaluating the Inclusiveness of Artificial Intelligence Soft...

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Artificial Intelligence, computer, Data Sciences and Applications (ACDSA), International Conference on

作者： Vasileios Alevizos Ilias Georgousis Akebu Simasiku Antonis Messinis Sotiria Karypidou Dimitra Malliarou Department of Learning Informatics Managament and Ethics Karolinska Institute Stockholm Sweden Department of Computer Science MLV Research Group International Hellenic University Kavala Greece Department Of Computer Software Engineering Zambia University Ndola Zambia HEDNO SA (Hellenic Electricity Distribution Network Operator) Department of Computer Science MLV Research Group International Hellenic University IntelliSolutions Athens Greece

The escalating integration of Artificial Intelligence (AI) in various domains, especially Project Management (PM), has brought to light the imperative need for inclusivity in AI systems. This paper investigates the role of AI software in augmenting both the inclusiveness and efficiency within the realm of PM. The research pivots around specific criteria that define and measure the inclusiveness of AI in PM, highlighting how AI, when developed with inclusiveness in mind, can significantly enhance project outcomes. However, there are inherent challenges in achieving this inclusiveness, primarily due to biases embedded in AI learning databases and the design and development processes of AI systems. The study offers a comprehensive examination of AI's potential to revolutionize PM by enabling managers to concentrate more on people-centric aspects of their work. This is achieved through AI’s ability to perform tasks such as data collection, reporting, and predictive analysis more consistently and efficiently than human counterparts. However, the incorporation of AI in PM extends beyond mere efficiency; it represents a paradigm shift in the epistemology of PM, calling for a deeper understanding of AI's role and impact on society. Despite these advantages, the adoption of AI comes with significant challenges, particularly in terms of bias and inclusiveness. Biased AI learning databases, which use shared and reusable datasets, often perpetuate initially discriminatory algorithms. Moreover, unconscious biases and stereotypes of AI designers, developers, and trainers can inadvertently influence the behavior of the AI systems they create. This necessitates a paradigmatic shift in how AI systems are developed and governed to ensure they do not replicate or exacerbate existing social inequalities. The research proposes a methodological approach involving the development of criteria for inclusion and exclusion, alongside data extraction, to evaluate the inclusiveness and efficien

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Regression Models for Predicting Gamified Educational Outcomes

Regression Models for Predicting Gamified Educational Outcom...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ibrahim, Abdelhameed Gatzoulis, Christos Eid, Marwa M. Rizk, Faris H. Abualigah, Laith El-Kenawy, El-Sayed M. Isa Town Bahrain Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Applied Science Private University Applied Science Research Center Amman11931 Jordan Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan

ISBN: (纸本)9798350351408

This paper focuses on improving learning and involvement in studies by employing game elements and considering online learning platforms to deliver statistics curriculum. According to the problem statement, the research examines gamification methods to transform studying from a routine to having enjoyment while improving academic results. This area of investigation is being extended to predict academic outcomes by applying different machine learning models to student performance data received from gamified. The strategy entails using a pedagogy based on collecting data from students who participated in a gamified educational platform;the data comprises indicators like pre-and post-platform exam grades, grades on quizzes, and indicators of platform usage. Multiple regression algorithms, including Random Forest and Gradient Boosting, are applied to student performance. In the experiments, the output results indicate that the model of Random Forest Regressor outperforms other models with the least Mean Squared Error (MSE) of 0.052. This model performs the best in terms of prediction accuracy with competence level in predicting students' results in gamified environments. The findings can support decision-making in designing educational activities. The Random Forest Regressor model can be identified as the best predictive model and may help train gamified learning platforms, which can encourage student engagement that correlates to performance. © 2024 IEEE.

关键词： Prediction models

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