The demonstrative realisation of this research is the smart health monitoring system, proposed and designed to employ wearable IoT technology and predictive analysis for constant health check. The system is intended t...
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ISBN:
(纸本)9798350368109
The demonstrative realisation of this research is the smart health monitoring system, proposed and designed to employ wearable IoT technology and predictive analysis for constant health check. The system is intended to gather the physiological data;for example, pulse, blood pressure, and oxygen saturation and others via wearables and process it using sophisticated AI techniques and approaches, including deep learning based on CNN and LSTM. According to the systems, there are predictions that need to be offered by means of the programs and health risks that should be anticipated for timely intervention in order to increase chances for the patients' recovery. Although the system is designed for home use, the writers applied a cloud-based solution that provides scalability and real-time data processing;data is securely transferred from the wearable devices to the cloud through Bluetooth and Wi-Fi. This physiological data is augmented by the other public datasets available in the healthcare domain such that the AI learning algorithms can learn from an extensive health record. From the above research, it manifests how the system was able to predict serious health precursors including;heart attack and respiratory failure with LSTM models having prediction rate of more than 96%. It also delivers on-time alarms to the clinical practitioners, with average response time of less than three seconds for critical states. A set of surveys concerning the usability and comfort of the wearable devices among the patients has been conducted and the results show high satisfaction levels reported by the patients about the accuracy as well as simplicity of the system. Despite the introduced effort of the system, there exist significant concerns involving data protection, compatibility, and AI ethic;therefore, the future study should overcome such shortcomings. With these advancements of an artificial intelligence driven health monitoring system, healthcare systems would shift to being mor
The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with...
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Nowadays, the demand for language learning is increasing because people need to communicate with other people belonging to different regions for their business deals, study, etc. During language learning, a lot of pro...
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Purpose: Virtual reality (VR) technology invaded various domains including architecture practice and education. Despite its high applications in architecture design education, VR has a high potential to be used in arc...
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Purpose: Virtual reality (VR) technology invaded various domains including architecture practice and education. Despite its high applications in architecture design education, VR has a high potential to be used in architectural history courses as well. This paper aims to examine the effect of using VR technology on the students’ learning abilities of history of architecture. Design/methodology/approach: The experimental approach was used. Two experiments were designed by creating virtual environments for two selected architectural examples from the Modern architecture course. The participants who were students of Modern architecture class had to complete two questionnaires, one for each example. The first one was based on Bloom’s taxonomy, and the other was prepared to test the participants’ analytical and critical skills. Besides, participants had to fill out satisfaction and ease-of-use survey on a five-step Likert scale. Findings: Participants in the VR condition achieved better grades in knowledge gain compared to those in the traditional conditions. Their analytical and critical thinking skills were improved in the VR conditions. Gender has a significant impact on analytical and critical thinking skills. Participants recorded a high level of satisfaction;however, male students were more satisfied than female students who reported concerns about the weight of the used tools and nausea symptoms. Research limitations/implications: This study informs architecture education and provides insights into the potentials of using advanced technology in architectural history education. Teaching the various history of architecture courses will be improved, shifted toward a more student-centered curriculum, and may acquire more excitement and conscious curiosity. Originality/value: Using VR in architectural education is rigorous in architectural design courses and students’ design projects’ presentations. This research expands architectural education research by examining ot
For several years, traffic congestion has been a major problem in big cities where the number of cars and different means of transportation has been increasing significantly. The problem of congestion is becoming more...
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Text entry is a fundamental and ubiquitous task, but users often face challenges such as situational impairments or difficulties in sentence formulation. Motivated by this, we explore the potential of large language m...
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On February 7, 2021, a rockfall in the Rishiganga Valley killed about 200 people. For the integration of seismic, social media, and remote sensing, a case study on this tragedy has been conducted. This study explores ...
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A multiband tri-branch monopole antenna is presented. The antenna structure is constructed of radiating patches with three branches. It is fed by a microstrip transmission line. Essentially, the antenna is a simple st...
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Understanding the regions where proteins bind to peptides is vital for studying diseases like cancer, as it sheds light on various cellular processes and aids in drug discovery. Although researchers can experimentally...
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ISBN:
(数字)9798331523114
ISBN:
(纸本)9798331523121
Understanding the regions where proteins bind to peptides is vital for studying diseases like cancer, as it sheds light on various cellular processes and aids in drug discovery. Although researchers can experimentally investigate protein-peptide interactions at the region level by analyzing the structures of their complexes, only a limited number of these structures are known. Additionally, experiments to determine these interactions can be both costly and time-consuming. Computational methods provide an alternative and complementary approach to experimental techniques. As a result, using computational methods to predict peptide-binding regions can significantly enhance the efficiency and cost-effectiveness of experimental research. To leverage these advantages, this study introduces a new method named Structure-based Prediction of Residue-level Interaction of Peptides (SPRINT-Pep), designed to pinpoint the binding regions between proteins and peptides. This approach leverages two distinct predictive models: a classification-based model combining DeepResNet with a Support Vector Machine (SVM) and a segmentation-based model called DeepU-Net. Our predictions outperform other state-of-the-art methods in terms of F-measures (by at least 23%), precision (by at least 3.7%), and the balance between sensitivity and specificity (by about 7.7%). These findings demonstrate the proficiency of the proposed method.
Applications running on an Internet of Things (IoT) device are usually deployed in an untrusted environment. This introduces risks of vulnerability to malware, and loss of intellectual property associated with securit...
Applications running on an Internet of Things (IoT) device are usually deployed in an untrusted environment. This introduces risks of vulnerability to malware, and loss of intellectual property associated with security sensitive code. Trusted execution environments (TEEs) and TEE-based applications have been widely adopted to run security sensitive workloads and protect the security of applications. However, existing approaches require specialized CPU support or hardware peripherals equipped with co-processors, precluding widely deployment on low-cost IoT devices. In this paper, we propose a flash memory controller-based collaborative execution environment (FMC-CEE), a lightweight security solution constructed on the target flash device to provide code confidentiality and basic security primitives for low-cost IoT devices and embedded devices. FMC-CEE leverages the microprocessor of the target flash device as a co-processor that executes security-sensitive workloads collaboratively with the target system. We implemented a prototype of FMC-CEE on a Trans-Flash (TF) card and executed security-sensitive tasks of the target host. The experimental results show that FMC-CEE takes $590.748 \mu \mathrm{s}$ to execute the remote code (512 bytes), thus incurring very little overhead on the host system.
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