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Privacy-Enhanced Data Deduplication Computational Intelligence Technique for Secure Healthcare Applications

Computers, Materials & Continua 2022年第2期70卷 4169-4184页

作者： Jinsu Kim Sungwook Ryu Namje Park Department of Convergence Information Security Graduate SchoolJeju National University63294Korea Master’s Program in Future Strategy Korea Advanced Institute of Science and Technology34141Korea Department of Computer Education Teachers CollegeJeju National University63294Korea

A significant number of cloud storage environments are already implementing deduplication *** to the nature of the cloud environment,a storage server capable of accommodating large-capacity storage is *** storage capacity increases,additional storage solutions are *** leveraging deduplication,you can fundamentally solve the cost ***,deduplication poses privacy concerns due to the structure *** this paper,we point out the privacy infringement problemand propose a new deduplication technique to solve *** the proposed technique,since the user’s map structure and files are not stored on the server,the file uploader list cannot be obtained through the server’s meta-information analysis,so the user’s privacy is *** addition,the personal identification number(PIN)can be used to solve the file ownership problemand provides advantages such as safety against insider breaches and sniffing *** proposed mechanism required an additional time of approximately 100 ms to add a IDRef to distinguish user-file during typical deduplication,and for smaller file sizes,the time required for additional operations is similar to the operation time,but relatively less time as the file’s capacity grows.

关键词： Computational intelligence cloud multimedia data deduplication

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Classification and Prediction of Knee Osteoarthritis by Deep Learning Approach 3rd

Classification and Prediction of Knee Osteoarthritis by Deep...

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3rd International Conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Saraswat, Amit Tooley, Tanya Shrivastav, Snigdha Srivastava, Devesh Kumar Department of Electrical Engineering Manipal University Rajasthan Jaipur303007 India Senior Analyst EY Haryana Gurgaon India Master of Science in Data Science program at the University of Rochester New York United States Department of Information Technology Manipal University Rajasthan Jaipur303007 India

ISBN: (纸本)9789819784288

The purpose of this research work on medical image analysis is to identify and diagnose knee osteoarthritis (OA) utilizing medical images by applying machine learning techniques. Knee OA is a degenerative joint disease that is becoming more prevalent in aged people worldwide. The study examines the effectiveness of several machine learning algorithms, including deep learning, to analyze medical images of knee OA, such as X-rays and magnetic resonance imaging (MRI). The research findings suggest that using machine learning algorithms might greatly improve the accuracy and efficacy of detecting knee OA, especially in its early stages. Moreover, machine learning algorithms can assist in the creation of specialized treatment plans by predicting the progression of the disease and recommending appropriate remedies. The findings of this study show how machine learning-based medical image analysis has the potential to enhance patient outcomes and quality of life for those with knee OA. Cancer detection diagnosis, neurological condition identification, and cardiovascular function analysis are a few instances of medical picture analysis employing machine learning. Moreover, image-guided treatments have been made more accurate and surgical planning has been made easier with the use of machine learning algorithms. Various methods are applied to achieve a test accuracy of 76.10% which is just 5% less than the accuracy provided by the Kellen-Lawrence Grading Scale. Overall, machine learning-based medical image analysis has the potential to revolutionize medical practice by facilitating earlier and more precise diagnosis, individualized treatment planning, and improved patient outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diseases

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BEST: AN INSTRUCTIONAL DESIGN MODEL TO EMPOWER GRADUATE STUDENT SELF-EFFICACY IN RESEARCH METHODOLOGY THROUGH TPACK INTEGRATION

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Interdisciplinary Journal of information, Knowledge, and Management 2025年 20卷

作者： Sukma, Narongsak Pum, Wachiranun Master of Science Program in Professional Cyber Security Faculty of Digital Siam Technology College Bangkok Thailand Faculty of Management Science and Information Technology Nakhon Phanom University Nakhon Phanom Thailand

Aim/Purpose Traditional research methodology education faces challenges in developing student self-efficacy and integrating modern technology, necessitating innovative instructional approaches for graduate students. Background This study introduces the BEST model (begin with learner analysis, establish clear learning objectives, select engaging learning activities, and tailor instruction using technology) to address these challenges through systematic TPACK integration. Methodology A qualitative investigation was conducted with 10 graduate students through focus groups and in-depth interviews over one month, analyzed using narrative and content analysis approaches. Contribution Content analysis revealed improvements in self-efficacy (from four to eight participants), peer learning (from three to six), and critical thinking (from three to six) while maintaining active learning engagement throughout implementation. Findings Students showed significant improvements in self-efficacy (four to eight participants), peer learning (three to six), and critical thinking (three to six) while maintaining active learning engagement. Recommendations for Practitioners Institutions should implement robust technical support systems and flexible learning pathways while ensuring adequate infrastructure before adoption. A phased implementation approach with peer mentoring is recommended to address technology integration challenges. Recommendations for Researchers Future research should examine the BEST model’s effectiveness across different cultural contexts and disciplines through comparative and longitudinal studies while exploring the integration of emerging educational technologies. Impact on Society The BEST model enhances the quality of graduate research education by developing students’ confidence and competence, potentially leading to more capable researchers and improved research outcomes. Future Research Longitudinal studies should track the impact on research productivity and

关键词： Students

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Environmental Attitude towards Green Purchasing Behavior in Jakarta

Environmental Attitude towards Green Purchasing Behavior in ...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Sanny, Lim Chairuddin, Lingga Ninal, Melvin M. Bina Nusantara University Binus Business School Master Program Management Department Jakarta Indonesia Bina Nusantara University Binus Business School Undergraduate Program Management Department Jakarta Indonesia St. Paul University - Surigao Information Technology Surigao City Philippines

ISBN: (纸本)9798350337617

Climate crisis is real if no movement for change are made. The outcome is disastrous and will affect numerous aspects including businesses and international trade between countries. Therefore, the objective of this research is to find whether there is a significant influence towards green purchase intention of environmental knowledge and environmental concern mediated by environmental attitude. The research type is quantitative and associative to find the causal relationship. 228 respondents who live in Jakarta and have purchased green products are obtained by purposive sampling method. The findings show purchasing intentions for environmentally friendly products or "green"purchases are significantly influenced by environmental knowledge, environmental care, and environmental attitude. The mediating variable, environmental attitude played a great role and strengthened the relationship compared to the sole direct effect. However, environmental concerns influence green purchase intention more significantly compared to environmental knowledge. information about how bad the current environment should be more disseminated and informed more efficiently and effectively. © 2023 IEEE.

关键词： International trade

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Tuberculosis Detection Based on Multiple Model Ensemble in Chest X-ray Image 10

Tuberculosis Detection Based on Multiple Model Ensemble in C...

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10th International Conference on Orange technology, ICOT 2022

作者： Tsai, An-Chao Zhou, Zhe-Guang Ou, Yang-Yen Wang, I Jhing-Fa International Master Program of Information Technology and Application National Pingtung University Pingtung Taiwan National Cheng-Kung University Department of Electrical Engineering Tainan Taiwan

ISBN: (纸本)9781665454193

Tuberculosis is a legal infectious lung disease, a chronic infectious disease caused by tuberculosis. The diagnosis of the confirmed person is usually judged by chest X-ray images, and the sputum smear is used to detect the presence of tuberculosis in the sputum. Screening high-risk individuals can effectively prevent the spread of tuberculosis;however, chest X-ray image interpretation and sputum testing take a long time. When infectious tuberculosis patients are found, tuberculosis has already spread. We propose a tuberculosis diagnostic triage system and a tuberculosis detection system based on preprocessing of lung image segmentation. In the tuberculosis diagnosis and triage system, AI interpretation is performed immediately after the patient's chest X-ray is taken, and abnormal tuberculosis cases are immediately listed. Lung field cropping through U-Net for classification in the form of a multiple ensemble system, including the use of EfficientNet-BO, Densenet121, and Xception. After marking abnormal lung images, we use a tuberculosis detection system to find patients with tuberculosis and use GRAD-Cam to display the image areas that are of most interest to the model, similar to a heat map, allowing us to know the model Predicting focus areas for tuberculosis. We used specificity, sensitivity, accuracy, and area under the receiver operating characteristic curve (AUROC) to evaluate the classification model performance. © 2022 IEEE.

关键词： Image segmentation

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Enabling Smart Home Energy Management through Gesture-Based Control and IoT technology

Enabling Smart Home Energy Management through Gesture-Based ...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Khairul Hawani Rambe Chia-Ho Ou International Master Program in Information Technology and Applications Department of Computer Science and Information Engineering National Pingtung University Taiwan

The Internet of Things (IoTs) have become ubiquitous in all aspects of public needs today. The application of IoT technology is crucial for promoting energy-saving behavior. This paper presents a control system used for smart home devices based on hand gesture control and Android applications. The hardware design uses the ESP32 microcontroller, which has an in-chip Wi-Fi module, making it very suitable for creating IoT application systems. The software was designed using the Arduino IDE, and the application display design was developed using Kodular Creator, which is used to control the lamps and switches. The experiment results show that the electrical equipment can be activated using the Android application with gesture sensors by waving down, up, left, and right.

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A Friendly Interface Medical Service Robot with Fault-tolerant Voice Recognition and Mobile Phone Remote Control 10

A Friendly Interface Medical Service Robot with Fault-tolera...

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10th International Conference on Orange technology, ICOT 2022

作者： Ou, Yang-Yen Tsai, An-Chao Hsu, Pin-Yang Chen, Che-Wen Wang, Jhing-Fa National Cheng-Kung University Department of Electrical Engineering Tainan Taiwan National Pingtung University International Master Program of Information Technology and Application Pingtung Taiwan

ISBN: (纸本)9781665454193

In recent years, due to the impact of COVID-19 around the world, there has been a serious shortage of medical resources. In order to supplement the manpower and fear that medical staff's contact with patients will cause a breach in the epidemic, reduce the workload of nurses, and help nurses perform repetitive tasks so that nurses can concentrate more on the patient's condition. Therefore, this paper proposes M-Robot, which is a friendly interface service robot based on the Android system and can be controlled by voice, touch, and remote control in the medical care field. The system is mainly divided into two parts. One is the web server. The web server is divided into two parts: front-end and back-end. The front-end is responsible for friendly user interface management, and the back-end is for accessing the SQLite database, as well as processing speech recognition and semantic understanding in voice services. In the other part, we use TEMI robot to develop and complete the desired service. Its service content includes environment introduction, delivery service, questionnaire survey, broadcast car, scheduling reminder, follow-up record, and patient instruction video. In the voice control mode, the user can say the wake-up word to the robot and say the required service content, and the robot will execute after receiving the message;in the remote control mode, we provide a friendly web interface for remote control. As well as the information needed to manage various services. © 2022 IEEE.

关键词： Remote control

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3D object detection combined with inverse kinematics to achieve robotic arm grasping 10

3D object detection combined with inverse kinematics to achi...

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10th International Conference on Orange technology, ICOT 2022

作者： Chen, Che-Wen Tsai, An-Chao Zhang, Yu-Hong Wang, Jhing-Fa National Cheng-Kung University Department of Electrical Engineering Tainan Taiwan International Master Program of Information Technology and Application National Pingtung University Pingtung Taiwan

ISBN: (纸本)9781665454193

In recent years, the research of autonomous robotic arms has received high attention from academia and industry, so this thesis aims to develop a visual detection system for robotic arms to grasp and place objects. Object detection technology The purpose of which is to locate objects of interest (target) in a given image, providing object location bounding boxes and categories. ROS is a set of open-source software libraries designed to simplify the creation of complex and robust on various robotic platforms. the task of the robot behavior. The proposed scheme uses a camera with depth information and combines target localization and edge detection algorithms to accurately measure the relative distance between the object and the robot arm. The process includes deep neural network construction and use, robotic arm positioning, object bounding box position information, and robotic arm movement control. Considering the visual scale, the relative distance between the target and the robotic arm is calculated so that the robotic arm can grasp the target and place the object in a specific position. This paper introduces how to combine the deep learning object detection model and ROS moveit! to complete the object identification and grasp and place the robotic arm. © 2022 IEEE.

关键词： Object detection

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Application of Named Entity Recognition by Self-Attention BiLSTM-CRF to Chinese Prescription Document 10

Application of Named Entity Recognition by Self-Attention Bi...

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10th International Conference on Orange technology, ICOT 2022

作者： Tsai, An-Chao Lee, Tsung-Yen Ou, Yang-Yen Wang, Jhing-Fa National Pingtung University International Master Program of Information Technology and Application Pingtung Taiwan National Cheng-Kung University Department of Electrical Engineering Tainan Taiwan

ISBN: (纸本)9781665454193

Reporting the contents of the prescription is a time-consuming and labor-intensive task for pharmacists, and it is easy to make mistakes in this kind of work that requires repeated confirmation under the time pressure of customers. Because medicines usually have an impact on the human body, if the wrong medicine is accidentally provided to the customer, it may even lead to death. Therefore, in our work, we train a bidirectional LSTM-CRF network combined with an attention mechanism to perform the NER task on prescription notes and extract key prescription declaration information. The semi-automatic labeling module was implemented using a rule-based approach, and a total of 636 different prescription notes were trained and tested, including hospitals, clinics, and medical centers. The identification accuracy rate is more than 90 % in each field of declaration information. Provides a UI interface for subsequent identification to facilitate pharmacists to modify the identification content and declare key information. © 2022 IEEE.

关键词： Hospitals

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Autism Spectrum Disorder Detection using Autistic Image Dataset

Autism Spectrum Disorder Detection using Autistic Image Data...

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International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

作者： Ritesh Chandra Sadhana Tiwari Ashutosh Kumar Sonali Agarwal M. Syafrullah Krisna Adiyarta Information Technology Department Indian Institute of Information Technology Allahabad Prayagraj India Program of Master of Computer Science Universitas Budi Luhur Indonesia

Autism spectrum disorders (ASD) are neurodevelopmental disorders that are marked by enduring difficulties with speech, nonverbal communication, and restricted or repetitive behaviors. Early detection and intervention can greatly improve outcomes for people with ASD. Recently, deep learning algorithms have been applied to aid in the early detection of ASD using facial images. In this work, modifications of the commonly used VGG16 and VGG19 models for image recognition tasks are proposed to improve the performance of detecting ASD from a child’s frontal face image. The proposed model is unique, as it alters the architecture of existing models, adds an attentional mechanism, and applys transfer learning. These changes are intended to decrease the chance of overfitting and enhance the model’s capacity to capture subtle face characteristics. The performance of the updated model is assessed through accuracy, which is 82.55% for VGG19 and 80% for VGG16 model, and contrasted the outcomes of the original model. Performance of the modified model is also compared with that of the original model. The obtained results show that the modified model outperforms in detecting ASD from facial images, suggesting that the proposed modification is non-invasive for early detection of ASD and has the potential to contribute to the development of efficient tools.

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