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2024 International Conference on Smart Devices, ICSD 2024

作者： Sinha, Aashna Sharma, Nishesh Kumar, Shivam Lande, Ayapal Iqbal, Mohammed Ismail Uttaranchal University School of Applied and Life Science Division of Research & Innovation Dehradun India Uttaranchal University Aerospace Engineering Uttaranchal Institute of Technology Dehradun India SR University School of Computer Science & Artificial Intelligence Warangal India University of Technology and Sciences College of Engineering and Technology Nizwa Oman

ISBN: (纸本)9798350394412

An important area of attention is energy efficiency since artificial intelligence (AI) algorithms may control power use by identifying occupant habits and modifying systems accordingly. This not only lessens the impact on the environment but also generates significant financial savings over time. A new age of ease, effectiveness, and security for homeowners has arrived with the integration of AI in smart homes. Machine learning algorithms are used in AI-powered smart homes to automate and improve a variety of daily tasks. These solutions, which range from voice-activated virtual assistants to intelligent lighting and climate management, adapt and react to the particular tastes and demands of residents. AI-driven surveillance substantially improves security by utilizing tools like face recognition, predictive analytics, and remote monitoring to provide users piece of mind. Smart homes powered by AI provide advantages in terms of healthcare and wellbeing in addition to convenience and efficiency. Modern home-based health monitoring devices may measure vital signs and give early warnings of potential health problems. Reminders for taking medications and assistance for seniors who want to age in place can improve quality of life. A unified environment that adjusts and anticipates inhabitant demands is created via interconnected gadgets and central control centers. Strong data protection and secure network protocols are necessary since cybersecurity and privacy are urgent issues. The key developments and future ramifications of AI-driven smart homes are summarized in this study. © 2024 IEEE.

关键词： Atificial Intelligence Machine learning Smart home

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AI Based Prediction for Heart Disease: A Comparative Analysis and an Improved Machine Learning Approach 6

AI Based Prediction for Heart Disease: A Comparative Analysi...

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6th Asian Conference on Artificial Intelligence technology, ACAIT 2022

作者： Raval, Jay Verma, Jai Prakash Islam, Sardar N.M. Jain, Rachna Thakur, Narina Institute of Technology Nirma University Computer Science Engineering Department Ahmedabad India Victoria University Applied Informatics Research Melbourne Australia Bhagwan Parshuram Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665453110

Heart disease problems are growing day by day in the world. Many factors are responsible for increasing the chance of heart attack and any other disease. Many countries have a low level of cardiovascular competence in predicting heart disease-related issues. Finding the best accurate machine learning classifiers for various diagnostic uses by data mining and machine learning techniques aids in predicting whether or not the heart disease-related issue will occur. To predict heart disease, a number of supervised machine-learning algorithms are used and their effectiveness are evaluated. With the exceptionof MLP and KNN, all applied algorithms had their estimated feature significance scores for each feature. This helps to find the main factors affecting heart disease and the accuracy of the model, which helps to get the best prediction. At the end of the research the support vector machine gives us 87.91 % highest testing accuracy compare with all applied machine learning algorithm. © 2022 IEEE.

关键词： Data mining

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Low-Complexity Channel Estimation for RIS-Assisted ISAC System

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China Communications 2025年第4期22卷 296-308页

作者： Chen Zhen Li Jianqing Zhang Haijun Zhang Wei The Institute of Microelectronics University of MacaoMacao 999078China School of Computer Science and Engineering Macao University of Science and TechnologyMacao 999078China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services University of Science and Technology BeijingBeijing 100083China Henan Academy of Science Applied Physics Institute Co. LtdZhengzhou 450008China

Integrated sensing and communication(ISAC),assisted by reconfigurable intelligent surface(RIS)has emerged as a breakthrough technology to improve the capacity and reliability of 6G wireless ***,a significant challenge in RIS-ISAC systems is the acquisition of channel state information(CSI),largely due to co-channel interference,which hinders meeting the required reliability *** address this issue,a minimax-concave penalty(MCP)-based CSI refinement scheme is *** approach utilizes an element-grouping strategy to jointly estimate the ISAC channel and the RIS phase shift *** previous methods,our scheme exploits the inherent sparsity in RIS-assisted ISAC channels to reduce training overhead,and the near-optimal solution is derived for our studied RIS-ISAC *** effectiveness of the element-grouping strategy is validated through simulation experiments,demonstrating superior channel estimation results when compared to existing benchmarks.

关键词： channel estimation element grouping integrated sensing and communication(ISAC) RIS

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TARGET-tool: Participatory Design of an Interactive Professional Development Tool for Secondary School Physical Education Teachers 23

TARGET-tool: Participatory Design of an Interactive Professi...

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9th International Conference on Frontiers of Educational Technologies, ICFET 2023

作者： Weeldenburg, Gwen Kromkamp, Len Borghouts, Lars Verburg, Pepijn Hansen, Nicolai Brodersen Vos, Steven School of Sport Studies Fontys University of Applied Sciences Netherlands Department of Industrial Design Eindhoven University of Technology Netherlands Department of Computer Science Aalborg University Denmark

ISBN: (纸本)9798400707353

Secondary school physical education (PE) teachers are continuously challenged to find ways to support students learning and motivate them for an active and healthy lifestyle. To address this complexity, continuing teacher professional development (TPD) is key. Technological tools can facilitate the effective delivery of TPD in this context. Successful implementation of this technology, however, is not self-evident. Based on the general aim of effectively integrating technologies in the educational process and focusing on the needs of educators, this study examines how the evidence-based theoretical TARGET framework for creating a motivating PE learning climate might be embedded into a digital professional development tool for PE teachers, useful in everyday practice. It presents a case study in which a multidisciplinary team of researchers, designers, and end-users iteratively went through several phases of need identification, idea generation, designing, development, and testing. By using a participatory approach, we were able to collect contextualized data and gain insights into users' preferences, requirements, and ideas for designing and engaging with the tool. Based on these insights the TPD TARGET-tool for PE teachers was ultimately developed. The most prominent characteristics of this tool are (1) the combination of an evaluative function with teaching strategy support, (2) the strong emphasis on ease of use due to the complex PE teaching context, (3) the avoidance of social comparison, and suggestions of normative judgment, and (4) the allowance for a high level of customization and teacher autonomy. © 2023 Owner/Author.

关键词： Human computer interaction

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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

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computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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LowResIR-Net: Lightweight Residual Network for ADL Recognition Using Low-Resolution Infrared Sensor Data 16

LowResIR-Net: Lightweight Residual Network for ADL Recogniti...

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16th International Conference on ENTERprise Information Systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand

Detecting activities of daily living (ADL) is crucial for supported living and medical monitoring. Traditional approaches rely on high-resolution sensors and computationally intensive algorithms, limiting their scalability and practical use in real-world settings. This research introduces LowResIR-Net, a compact deep residual network architecture specifically tailored for recognizing ADL utilizing low-resolution infrared sensor data. LowResIR-Net efficiently extracts key features from low-dimensional data by leveraging the advantages of infrared sensing, such as privacy protection and light resilience. Its architecture combines residual connections with depth-wise convolutions, enhancing feature learning while maintaining a compact model size. The model’s performance is evaluated using the Coventry-2018 dataset, containing low-resolution infrared data from three residential sensor locations. Our tests have shown that in all three cases, LowResIR-Net consistently achieves superior performance compared to other cutting-edge deep learning models, such as CNN, LSTM, BiLSTM, GRU, and BiGRU. LowResIR-Net attains the most excellent accuracies of 90.18%, 93.75%, and 93.30%, and F1-scores of 81.81%, 89.55%, and 88.92% for scenarios I, II, and III, respectively. Furthermore, it achieves these results with fewer parameters than most other models. LowResIR-Net’s strong performance highlights its ability to accurately capture complex patterns in low-resolution infrared data for ADL detection. This study demonstrates the potential of lightweight deep learning and low-resolution infrared sensors in developing privacy-focused and computationally efficient wellness monitoring systems. © 2025 The Author(s).

关键词： Infrared detectors

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A Real-Time Sentiment Analysis Model of Students by Fusing Attention Mechanism 13

A Real-Time Sentiment Analysis Model of Students by Fusing A...

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13th International Conference on Information technology in Medicine and Education, ITME 2023

作者： Hu, Pengyun Tang, Xianpiao Yang, Liu Xia, Daoxun School of Big Data and Computer Science Guizhou Normal University China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350319156

By recognizing students' facial expressions in a classroom, it is possible to determine in real-time whether students are confused, dissatisfied or happy. Understanding students' emotional states helps teachers adjust teaching strategies and methods, thus improving the quality and effect of teaching. However, classroom scenarios are often complex, and most previous expression recognition methods suffer from the problems of missing local features and low accuracy. Additionally, the imbalanced distribution of datasets has an impact on the recognition accuracy during model training. Therefore, in this paper, based on deep learning theory, an LCANet model, which incorporates an attention mechanism, is proposed to realize the recognition and analysis of students' expressions during classroom teaching. To optimize the global feature extraction capability of the network, the model uses ConvNeXt V2 as the backbone. Moreover, to enable the model to better focus on the important parts of facial expressions, we add an improved channel space attention (CSA) module to extract local features. In addition, we introduce the LF loss function to alleviate the class imbalance problem in the facial expression dataset. The experimental results show that our LCANet model achieves good recognition rate on the public FERPlus dataset with 91.43% accuracy. Therefore, the proposed LCANet model has good robustness and expression recognition performance. © 2023 IEEE.

关键词： Students

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MaSS: multi-attribute selective suppression for utility-preserving data transformation from an information-theoretic perspective 24

MaSS: multi-attribute selective suppression for utility-pres...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yizhuo Chen Chun-Fu (Richard) Chen Hsiang Hsu Shaohan Hu Marco Pistoia Tarek Abdelzaher Department of Computer Science University of Illinois Urbana-Champaign and Global Technology Applied Research JPMorgan Chase Global Technology Applied Research JPMorgan Chase Department of Computer Science University of Illinois Urbana-Champaign

The growing richness of large-scale datasets has been crucial in driving the rapid advancement and wide adoption of machine learning technologies. The massive collection and usage of data, however, pose an increasing risk for people's private and sensitive information due to either inadvertent mishandling or malicious exploitation. Besides legislative solutions, many technical approaches have been proposed towards data privacy protection. However, they bear various limitations such as leading to degraded data availability and utility, or relying on heuristics and lacking solid theoretical bases. To overcome these limitations, we propose a formal information-theoretic definition for this utility-preserving privacy protection problem, and design a data-driven learnable data transformation framework that is capable of selectively suppressing sensitive attributes from target datasets while preserving the other useful attributes, regardless of whether or not they are known in advance or explicitly annotated for preservation. We provide rigorous theoretical analyses on the operational bounds for our framework, and carry out comprehensive experimental evaluations using datasets of a variety of modalities, including facial images, voice audio clips, and human activity motion sensor signals. Results demonstrate the effectiveness and generalizability of our method under various configurations on a multitude of tasks. Our source code is available at this URL.

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Guest Editorial: Special Issue of Selected Extended Papers From the IEEE International Conference on Flexible, Printable Sensors and Systems (FLEPS) 2024

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第1期4卷 2-3页

作者： Mantysalo, Matti Marrocco, Gaetano Agarwale, Shweta Grau, Gerd Gao, Wei Tampere University Faculty of Information Technology and Communication Sciences Tampere33720 Finland University of Roma Tor Vergata Dipartimento di Ingegneria Civile ed ed Ingegneria Informatica Rome00133 Italy Aarhus University Department of Electrical and Computer Engineering Aarhus8200 Denmark York University Department of Electrical Engineering and Computer Science TorontoONM3J 1P3 Canada Caltech Department of Engineering and Applied Science PasadenaCA91125 United States

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Deep Learning Enabled Microarray Gene Expression Classification for Data science Applications

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computers, Materials & Continua 2022年第11期73卷 4277-4290页

作者： Areej A.Malibari Reem M.Alshehri Fahd N.Al-Wesabi Noha Negm Mesfer Al Duhayyim Anwer Mustafa Hilal Ishfaq Yaseen Abdelwahed Motwakel Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In bioinformatics applications,examination of microarray data has received significant interest to diagnose *** gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of *** data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern *** paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics *** proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform ***,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical ***,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)*** utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification *** examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark *** extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.

关键词： Bioinformatics data science microarray gene expression data classification deep learning metaheuristics

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