Reviewerly/exHarmony huggingface dataset:
Design-Thinking-Driven Variational Autoencoder for Human Activity Classification

This study introduces a design-thinking-driven approach aimed at enhancing the accuracy and robustness of Human Activity Recognition (HAR) using sensor data from Internet of Things (IoT) devices. The proposed algorithm harnesses the synergy of a Variational Autoencoder-Based Dimensionality Reduction Technique with deep learning methods for human activity classification. HAR plays a pivotal role in categorizing activities, especially in domains like elder care and fitness tracking. This research work systematically assesses the model’s performance across diverse datasets, including WISDM, HAPT, HAR, and KU-HAR, utilizing performance evaluation metrics such as accuracy, recall, precision, and F1-Score. The results underscore the potential of this design-thinking-infused approach to significantly enhance the precision and reliability of activity prediction in IoT-based HAR applications. The study introduces a design-thinking-driven approach by combining Variational Autoencoder-Based Dimensionality Reduction with deep learning techniques for enhancing Human Activity Recognition (HAR).
ResNet and ResNeXt-Powered Kidney Tumor Detection: A Robust Approach on a Subset of the KAUH Dataset

Renal disorders, especially neoplasms, provide a global health risk. This study analyses 2,170 photographs from the 8,000-image KAUH dataset to identify kidney tumours. This study uses deep convolutional neural networks (CNNs) to identify kidney tumours by kind and stage. This project aims to create a reliable kidney tumour detection model.This study fuses two resilient CNN architectures, ResNet and ResNeXt, to improve the detection model. ResNeXt architecture solves gradient-related problems in deep learning models. The ResNeXt architecture allows multi-path networks with a cardinality choice. ResNeXt is ideal for feature extraction and learning due to its high scalability and gradient flow.Our research shows that our model is resilient, achieving 94% accuracy on a subset of 2,170 pictures from the KAUH dataset. This research uses ResNet-pretrained machine learning algorithms and ResNeXt’s gradient flow characteristics to improve healthcare outcomes.
Resource Scheduling using Enhanced Teaching– Learning-based Optimizer in Cloud Environment

Resource Scheduling (RS) in cloud environment is a significant task targeted at effective management and allocation of cloud resources in order to meet the changing user demands and applications while maximizing the system efficiency and utilization. Effective RS has gained more importance in the cloud environment for meeting the dynamic user demands, enhancing overall system performance, and optimizing resource utilization. In this research study, metaheuristic optimizing techniques have proved efficiency in tackling the RS convolution issues. This study introduces a new Resource Scheduling using Enhanced Teaching–Learning-Based Optimizer (RS-ELTBO) algorithm in Cloud Environment. The primary goal of the RS-ELTBO technique lies in the process of optimizing resource allocation and performance metrics. Applying ETLBO for resource scheduling in cloud environments can lead to improved resource utilization, reduced costs, and enhanced overall performance. The ETLBO algorithm makes use of an objective function to determine the fitness of considerable resource allocation and employs a teaching phase, learning phase, and exploration strategy to iteratively refine the solutions. The performance study of the RS-ELTBO system was tested on various aspects. The achieved values stated that the RS-ELTBO system reached improved scheduling results over other approaches.
Incorporating Intercultural and Interdisciplinary Methods in a Project-based Learning Course to Foster Global Leading Engineers

As the global community finds itself confronting unprecedented environmental, socioeconomic and technological problems and needs, engineering education is also faced with the challenges of meeting current and future demands to produce engineers who can work in multidisciplinary and multicultural teams globally. This work-in-progress paper describes how to incorporate intercultural and interdisciplinary methods into project-based learning in engineering education, taking the Flying Robot Program at the School of Engineering, Nagoya University, as an example. It also analytically reframes an emerging educational model—the differential approach, which contrasts with the conventional integrative approach by incorporating the relevant skills and mindset—in the context of Japanese engineering education.
On the Possibility of Measuring Learner’s Facial Expressions in On-Demand Learning Environments in Junior High School Students

In this research, we focus on the on-demand learning environment where learners study alone and examine the possibility of facial expression measurement as a method to grasp the state of learners. In the on-demand learning environment, it is considered that the learner’s emotions are less likely to be expressed in facial expressions than in general face-to-face learning or communication with others. Also, we don’t have sufficient information about what kind of emotions will represent. Therefore, in this paper, we construct a general facial expressions measurement system for measuring basic emotions, which appear commonly in all people, by image analysis and examine the possibility of grasping learners’ states from the facial expression analysis results. We extracted scenes in which the emotional states of the participants were assumed to be different and compared the results of emotional output in each scene. We adopted correct and wrong answers to the quiz task as a criterion for the differences in participants’ emotional states. The analysis results showed that the types of emotional output in scenes with different participants’ emotional states were similar. Still, there were differences in the output patterns of emotions. Based on these results, it is expected that a facial expression measurement system specialized for on-demand learning environments can be constructed by adjusting the facial expressions and emotions that appear in learning situations.
DIY Wind Turbines: A Low-Cost Smart ICPS for Educational Research

Industrial cyber-physical systems (ICPS) research combines many fields, including mechatronics, electrical, electronic, network, systems and software engineering. ICPS testbeds are often prohibitively expensive to purchase, store and maintain, and tied to vendor-specific tool chains, making them infeasible for small-scale experimental and student-led research.With the rising popularity of 3D printing and ubiquitous computing, it is now possible to rapidly and inexpensively build model ICPS for lab research. This paper presents the "from-scratch" construction of an open-source 3D-printed smart wind turbine as a case study. We cover 3D modelling and printing of the wind turbine parts, the hardware-software interfacing and controller design using Arduino and the ESP8266 embedded WiFi controller. Interested readers can build a similar smart wind turbine by downloading the guidance and resource files linked to this document within days.The smart wind turbine also serves as a rich ICPS research and education testbed to enable next-generation learning. In addition to discipline-specific tasks like incrementally optimising the 3D model, the interfacing and/or controller design, students and researchers can also use it for interdisciplinary research exploration. As an example, we show how the design and development of the smart wind turbine can be used to study, learn, and research requirements traceability. We employ graph databases to effectively model, represent, manage and trace requirements throughout the development of the smart wind turbine. The key outcome is a light-weight and efficient method for managing requirements using the Neo4j graph database implementation.
Online Tutoring and Plagiarism-Aware Authentic Assessment of Database Design Assignments

Absorbing the concepts of functional dependency theories and its application to database normalization probably are the driest of exercises students go through in a first database class. Instructors also spend an inordinate amount of time developing assignments and tests to assess learners’ competence on these subjects. Recent emergence of ChatGPT and several online calculators make it extremely challenging for instructors to administer authentic online assessments. In this paper, we introduce an online tutoring and authentic assessment system, called NoDD, with a focus on reducing online cheating and facilitating autonomous grading. We present a new approach to capturing the student’s depth of understanding of the subject matter for the purpose of both tutoring and assessment under the assumption that the students will have access to online resources and calculators.
The Eye Gaze Feature During Face-to-Face Discussion Between Two People

In this Work-in-process paper, we detail the progress of experiment aimed at developing an eye gaze-psychological state model that can adapt to educational scenarios. Throughout this experiment, a combination of qualitative analysis and quantitative analysis is employed. The measuring methods encompass the questionnaire survey, the use of wearable eye tracker Tobii Pro Glasses 3 and semi-structured interview. The experimental result indicates that compared with the surroundings, participants show a heightened interest in other’s face. Additionally, they show a preference to choose the visual conditions of sitting on the same side during discussion scenario. Although this result is based on a preliminary study in the "discussion scenario", it is of great significance for exploring the relationship among eye-gaze and psychological state in various educational types, and it also contributes to the further enhancement of the formulation of the model. Simultaneously, it can lay a solid foundation for finding methods to enhance interaction in different educational types.
Development of Metaverse Inquiry-Based Learning

Kobe Institute of Computing | Graduate School of Information Technology is working on the "High School Tankyu(inquiry) Project," which is centered on inquiry-based learning using ICT in higher education. This project is an effort to draw out the imagination and creativity of high school students by creating a town in a metaverse space using ICT, and to build a system that allows students to run on their own. Here, we report on our efforts to develop contents for city planning in the metaverse space and to implement them in the classroom.
Comparing the Performance Measures of Underwater Image Enhancement Through Improved CNN With Gaussian and Kalman Filter Method

Underwater imaging poses several challenges due to the complex interactions of light with the water medium, resulting in poor image quality characterized by color distortion, blurring, and low contrast. This project focuses on optimizing underwater image quality using an improved convolutional neural network (CNN) method combined with the gausian filter technique and the performance measures are compared with Kalman filter technique. The proposed method consists of two main stages. First, an enhanced CNN architecture is designed to effectively learn and extract informative features from underwater images. The architecture employs advanced techniques such as residual connections, batch normalization, skip connections, identification of region, colour correction, future extraction to improve the network's performance in capturing the intricate details of underwater scenes. Through extensive training on a large dataset of underwater images, the CNN model is trained to understand the complex characteristics of underwater distortion and enhance the images accordingly. In the second stage, gausian filter and Kalman filtersare applied separately to the CNN-enhanced images to improve the PSNR, MSE, Sensitivity, Time Correction, Specification, colour accuracy and contrast. This step effectively mitigates color cast and enhances the overall visual quality of the images.
Automated IoT Based Fog-Based Hand Wash System

COVID-19 has spread around the world with virtually no region left untouched. The frightening death rates and rapid spread of COVID-19 have prompted numerous nations and jurisdictions to develop preventative measures, and handwashing is a key component of each and every one of them. Our hands play a crucial role in the transfer of germs. It is a simple, primary preventive measure that most people can do independently. Handwashing with soap and water for at least 20s or the use of alcoholbased hand sanitisers when soap and water are not available is the first line of defense in stopping the spread of infection. Failure to properly wash our hands results in the cross-transmission of these organisms to others. To avoid becoming sick, it's crucial to keep your hands clean, but lathering and scrubbing takes time! We want to cut water waste by 20–65% while still having everyone follow the same handwashing regimen. However, if we washing our hands under the running faucet for a whole 20 seconds, water can be wasted. The suggested method provides an alternative method that uses fog to clean hands. The suggested system's processor and fog maker module both use ATmega328 micro controllers to make water into fog. To eliminate the need for touching, the procedure uses an ultrasonic sensor to identify hands. A signal from an ultrasonic sensor must be sent to the microcontroller in order for the fog maker module to turn on when the user places their hands within the device. Handwashing instruction and information have been prioritized in response to the present COVID-19 pandemic, both for the general public and for those working in the healthcare industry.
Generating and Defending Against Adversarial Examples for Loan Eligibility Prediction

In this paper, we investigate the critical problem of generating and defending against adversarial examples in the context of loan eligibility prediction. Adversarial examples are deliberately crafted inputs that are designed to deceive machine learning models, and they pose a significant risk to the accuracy of loan approval decisions. We explore a variety of adversarial attacks, such as poison and evasion attacks, and their potential implications for loan eligibility prediction models. The Fast Gradient Sign Method (FGSM) is proposed as a technique to generate adversarial examples, and its effectiveness is evaluated on a variety of loan eligibility prediction models. Additionally, we assess the efficacy of different defense strategies to counter adversarial attacks. Our findings highlight the alarming effectiveness of adversarial attacks in causing model misclassifications, and they establish FGSM as a powerful tool for generating adversarial examples. Furthermore, our research underscores the importance of implementing robust defenses to mitigate the impact of adversarial examples in loan eligibility prediction. It is our hope that our work will stimulate further research into this important area of machine learning security as a valuable contribution to this field.
A Robust Method of Hiding Patient Secret Data Using Hybridization of Hyperchaotic System with LSB Based Encryption for Medical Image

In this digital native world data security and data transmission especially medical images plays vital role and the process of data transfer between ends is very crucial and authentication is must. Here the idea is to improve security of medical image using multi level watermarking technique for healthcare application. Watermarking is the important method of including meaningful information for improving security and authenticity of medical data. Thus, the proposed method gives the improved security for healthcare applications. The ISODATA classifier which classifies images into nonregion of interest (NROI) and region of interest (ROI) in a medical image. Further to enhance authenticity, the Hyperchaotic System with LSB based encryption is applying to NROI where Secret Patient Information (SPI) is inserted. Then LSB based encryption is applied to generate encrypted version. The reverse technique is applied to retrieve the medical image and patient information. The Quantitative analyses have been made to justify the effectiveness of algorithm.
A Robust Blind Watermarking Technique for Improved Medical Image Security

Watermarking serves as a protective approach for medical data which ensures protection against illegal access. This new method uses wavelet decomposition to analyze and visualize images with varying resolutions, mirroring the human visual system. Watermarks are strategically added to the high-frequency subbands of wavelet-transformed images remaining imperceptible to the human eye, thereby enhancing the security of the watermarking scheme. However, the limitations of existing wavelets in detecting singularities across different frequency bands necessitated the development of novel filter designs capable of unveiling singularities in all orientations. Here it introduces innovative nontensor product wavelet filter banks, employing special symmetric matrices to uncover singularities comprehensively. Experimental investigations demonstrate the superiority of these new wavelet filter banks in singularity detection compared to conventional wavelets. Building upon these advancements, a modified significant difference watermarking algorithm is proposed, further enhancing the security of medical image data
Exploration of Smart Medical Systems for Efficient Recognition of Potential Sleep Disruption Cases Relying on Health Metrics

This study explores a state-of-the-art methodology for the efficient recognition of potential sleep disruption cases relying on health metrics, comparing it with six traditional methods. The proposed method combines wearable technology, advanced data analysis, and machine learning to continuously monitor and analyze a wide range of health metrics during sleep. The traditional methods include Polysomnography (PSG), Actigraphy, Electroencephalography (EEG) analysis, Sleep Questionnaires, Sleep Diary, and Actigraph Monitoring. The comparison is made based on performance parameters, accessibility, and cost efficiency. Results indicate that the Proposed Method outperforms the traditional methods in all key performance parameters - sensitivity, specificity, accuracy, precision, recall, and F1 score. The Proposed Method achieves a high sensitivity of 0.96, ensuring it correctly identifies a vast majority of sleep disruption cases, while maintaining a specificity of 0.93 to minimize false alarms. The accuracy, precision, recall, and F1 score are consistently higher for the Proposed Method, affirming its superior accuracy and reliability. Furthermore, the Proposed Method exhibits notable advantages in terms of accessibility and cost efficiency. It is highly accessible, leveraging widely available wearable devices and mobile applications, enabling real-time monitoring and continuous tracking of health metrics. This accessibility, coupled with its cost-effectiveness, offers a user-friendly and economical solution for individuals and healthcare providers. In conclusion, the proposed methodology, with its emphasis on smart medical systems and advanced data analytics, presents a significant leap forward in the field of sleep disruption recognition. Its performance and accessibility surpass traditional methods, making it a valuable tool for early detection and personalized intervention in sleep-related disorders. The methodology is poised to enhance the overall management of sleep disruption cases, ultimately contributing to improved well-being.
Unsupervised Medical Image Denoising Using CycleGAN: Improving Low-Dose CT Image Quality

Medical imaging plays a significant role in clinical diagnosis, but the use of low radiation doses in CT imaging can lead to poor image resolution and precise diagnosis. In this investigation, a novel method has been suggested to enhance low-dose CT (LDCT) images using an unsupervised deep learning model based on CycleGAN. The CycleGAN architecture enables us to perform image-to-image translation without the need for paired data, making it suitable for medical image denoising where matching high-dose CT (HDCT) images are often unavailable. Our model employs two generators for LDCT-to-HDCT and HDCT-to-LDCT translation, along with two discriminators to differentiate real and generated images in both domains. Extensive experiments demonstrate that our approach significantly improves the quality of LDCT images, yielding highfidelity HDCT-like images. This technique holds great promise for enhancing medical image quality and ultimately benefiting patient care.
Securing IoT-Based Home Automation Systems Through Blockchain Technology: Implementation

Cybersecurity is the large subject matter that connects everything, processes, units and technologies. Network safety is a set of policies and settings. They are the integrity, accessibility and availability of facts and networks. Confidentiality of Software and Hardware Use. Today's community threats are continuously evolving and the surroundings is an increasing number of complex. Similarly, the clever domestic has emerge as a low-security threat. That's why it is so vital to remain secure online. Due to the vulnerability of the clever domestic network, there are purposes in many areas, such as users, locations, data, etc. Some clever units may additionally be lacking from your clever home. The machine has been hardened so that passwords can be scrambled. Unencrypted passwords can be searched on the machine or in the software. The safety of the clever domestic community is pretty high. Prioritize linked gadgets to forestall hackers from gaining access to them. Access to touchy or private data. Failure to do so should put your complete clever domestic community at risk. This learn about builds a mannequin to analyze specific protection problems in clever domestic conversation networks. This lookup wants to use qualitative methods, such as anecdotes. A lookup evaluation was once fundamental to raise out this study. and Other applicable records on the secondary facts series techniques used. Several research have been carried out on the protection threats associated to clever domestic networks and the use of applied sciences derived from block chain technology. Reduce protection troubles and defend your intelligence. Secure your domestic network.
A Comprehensive Study on Artificial Intelligence Techniques for Oral Cancer Diagnosis: Challenges and Opportunities

The malignant nature of oral cancer is made evident by its low average chance of survival. Oncology and surgical treatment modality advancements have only modestly improved outcomes. The rapid progression and metastasis of this type of cancer, the lack of primary symptoms, the large number of risk factors, and the general lack of public knowledge of oral malignancy are all contributing causes to its high fatality rate. Recently, it has been suggested to use artificial intelligence (AI) methods for screening for oral cancer. By using AI techniques for identifying oral carcinoma, this study collects reviewed data from possibly accessible databases and different oral cancer diagnosis techniques, evaluates them against established techniques, and gives technical information with major conclusions. We also go through some difficulties, inefficiencies, and potential future research areas for the various medical image analysis methods. A subfield of AI called ML and DL has arisen during the past ten years, and its applications in the healthcare sector have produced remarkable outcomes at lower costs and higher efficiency. For the period of 2020 to 2023, this study investigates several datasets, algorithms, modalities, and key findings used in ML and DL methodologies for the identification of oral cancer. Deep learning beats traditional machine learning for identifying oral carcinoma in large datasets, according to results from prior studies. Research gaps identified in current studies suggest that practical and scientific research is urgently required for future healthcare improvement.