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Notes Application using Android Studio and Cloud-based Data Management 7

Notes Application using Android Studio and Cloud-based Data ...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Dodda, Ratnam Yanala, Maneesh Reddy Pavishetti, Srija Cvr College of Engineering Dept. of Computer Science And Information Technology Hyderabad India

ISBN: (纸本)9798350359299

In today's rapidly advancing technological land-scape, the widespread adoption of mobile applications has become a defining feature of our digital age. This research study presents a on the development of a mobile notes application using Android Studio, with the primary goal of offering users a versatile platform for creating and managing notes. The proposed notes application is designed with a simple and user-friendly interface, facilitating effortless note creation, editing, and deletion. Moreover, it has features like voice-to-text for making notes, text-to-speech for reading the notes aloud, note sharing for easily sharing ideas with others and image insertion for storing the photocopy of the written notes or diagrams based on user interest. This research work uses Android Studio as the integrated development environment (IDE) and utilizes the Java programming language for the app's core functionalities. Furthermore, it incorporates Firebase Authentication to ensure secure user account management and the Firestore for Database for seamless storage and retrieval of notes. This research study provides an essential tool for students, professionals, and anyone seeking a convenient means to keep track of vital information. By combining mobile application development tools and cloud-based data management like Firebase, the application simplifies how information is managed, aligning with the way we live in the digital age. © 2024 IEEE.

关键词： Authentication

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Intelligent IoT Platform for Real-Time Monitoring of Indoor Air Quality in Healthcare Facilities 12

Intelligent IoT Platform for Real-Time Monitoring of Indoor ...

引用

12th International Conference on Orange Technology, ICOT 2024

作者： Cheng, Yin-Ting Dept. Computer Science and Information Engineering SHU-TE University Kaohsiung Taiwan

ISBN: (纸本)9798331523602

In recent years, people have been confronted with unprecedented emerging infectious diseases. Medical institutions typically manage high-risk infectious diseases, which are transmitted via droplets or airborne routes, by using negative pressure isolation rooms to house patients and provide medical care. These isolation areas and rooms are specially designed to effectively prevent the spread of pathogens, with the goal of protecting healthcare personnel and preventing large-scale hospital-acquired infections. As most medical institutions have already completed the installation of hardware, wired network construction is not ideal for hospital environments, as it would require modifications to existing infrastructure, indirectly affecting the quality of the hospital environment, such as noise from construction and the layout of piping systems. Therefore, this study proposes an "Intelligent IoT Platform for Real-Time Monitoring of Indoor Air Quality in Healthcare Institutions." The system leverages smart sensors integrated with the Internet of Things (IoT), utilizing the small size and ease of deployment of the sensors as part of the overall technical framework. The platform also incorporates health and safety indicators relevant to hospital hygiene, enabling the monitoring of various environmental factors to ensure the safety of healthcare personnel. © 2024 IEEE.

关键词： Indoor air pollution

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Improving Time Series Forecasting Accuracy with Transformers: A Comprehensive Analysis with Explainability 3

Improving Time Series Forecasting Accuracy with Transformers...

引用

3rd International Conference on Electrical, Electronics, information and Communication Technologies, ICEEICT 2024

作者： Joseph, Susmera Jo, Ashly Ann Raj, Ebin Deni Indian Institute of Information Technology Dept. of Computer Science Engineering Kottayam India

ISBN: (纸本)9798350369083

Time series forecasting is crucial in numerous sectors, including healthcare, energy, and finance. Transformer models, initially designed for natural language processing applications, demonstrate potential in capturing temporal dependencies within sequential data. In this paper, the application of transformer models in time-series forecasting is explored, and their performance and interpretability are investigated. Transparent models build trust and facilitate decision-making, especially in industries with ethical guidelines. While inherently interpretable time-series Transformers exist, there is a gap in research regarding post hoc methods, which is an area ripe for exploration. Understanding and comparing complex Transformer models for time series forecasting can benefit from advancements in explainability, ensuring accurate and comprehensible predictions. In comparison to five other transformer-based models and classical models, the Temporal Fusion Transformer (TFT) model outperformed them in short-term forecasting. Further enhancement of the model performance was achieved through a meticulous analysis of the Encoder-Decoder's importance, which contributed significantly to refining the forecasting accuracy. The vanilla transformer was explained using permutation feature importance. The model stability was analyzed using perturbation methods. © 2024 IEEE.

关键词： Spatio-temporal data

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Enhanced Machine Learning Approach for Detecting Spammers in Industrial Mobile Cloud Computing Environment 5

Enhanced Machine Learning Approach for Detecting Spammers in...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Bhavana, K. Sai Abhinandu, B. Sriyesh, P. Sai Rama, K. Satheesh Kumar, S. Institute of Aeronautical Engineering Dept. Computer Science & Information Technology Hyderabad India

ISBN: (纸本)9798331518097

This research study proposes an innovative machine learning algorithm, Spammer Identification Novel K-Means Extension (SINKEX), to fortify IoT mobile networks against commands and potential disruptions in industrial production. Utilizing an industrial mobile cloud dataset, the algorithm employs basic, content, and network features, including follower count, post frequency, message content, and hyperlink usage, for spam detection. After employing Pearson Correlation and Euclidean Distance to eliminate dissimilar records, Principal Component Analysis (PCA) is applied to remove irrelevant features. The SINKEX algorithm then labels unclassified records, designating an account as a SPAMMER or NON-SPAMMER based on features like follower count and post frequency. Comparisons with a hybrid fuzzy c-means clustering algorithm demonstrate SINKEX's superior performance, achieving higher accuracy, precision, and recall. The results are presented through an intuitive graphical user interface, showcasing the algorithm's effectiveness in securing industrial IoT networks against malicious activities. © 2024 IEEE.

关键词： Adversarial machine learning

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Benchmarking Network Functionality: Performance Evaluation of SDN Controllers on Different Network Functions 100

Benchmarking Network Functionality: Performance Evaluation o...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Monir, Md Fahad Fawad Hasan, Azwad Hoque, Md Mozammal Ahmed, Tarem Granelli, Fabrizio Independent University Dept. of Computer Science and Engineering Bangladesh University of Houston Cullen College of Engineering TX United States Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798331517786

Network Softwarization revolutionizes infrastructure by introducing programmable network functions, leading to scalable, agile, and efficient networks. This innovation facilitates adaptable, secure Next Generation Networks, transforming connectivity. This study conducts a comparative analysis of SDN-based network functions utilizing three different controllers: Open vSwitch (OVS), OpenFlow Reference controller, and RYU, to understand their impact on packet loss and jitter. The OpenFlow Reference controller, primarily used for educational and basic experimental purposes, serves as a baseline. The OVS controller, operating within the Open vSwitch framework in Mininet, offers a practical level of network management capability, more advanced than the OpenFlow Reference controller yet not as comprehensive as the RYU Controller, a production-grade SDN controller. Our research aims to fill the gap in comparative performance analysis of these controllers, particularly in hybrid round-robin-based load balancing and firewall implementations. Recognizing the operational reality of SDN deployments, we explore both reactive and proactive modes, acknowledging that real-world applications like firewalls and load balancers often operate proactively with pre-populated rules, affecting data plane performance. The findings provide insights into each controller's effectiveness, with OVS and RYU showing enhanced capability in reducing packet loss and jitter compared to the OpenFlow Reference controller. This research not only sheds light on the functional behaviors and performance trade-offs of these controllers but also contributes to the broader SDN and NFV discourse, offering a foundation for future academic and practical advancements in the field. © 2024 IEEE.

关键词： Packet loss

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An Efficient Approach for Maintaining and Mining Frequent Episodes 6

An Efficient Approach for Maintaining and Mining Frequent Ep...

引用

6th International Conference on Artificial Intelligence in information and Communication, ICAIIC 2024

作者： Yen, Show-Jane Lee, Yue-Shi Ming Chuan University Dept. of Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9798350344349

Data mining technology plays a crucial role in the area of data analysis. Mining frequent episodes stands out as a pivotal task within this domain, enabling users to forecast future events based on present occurrences. Conventional methods for discovering frequent episodes typically follow a hierarchical approach, involving the generation of candidate episodes and subsequent scanning of sequence data to count their frequency, which can be quite time-consuming, as it necessitates repeated scans of the sequence data and the search for candidate episodes. In this paper, we introduce a novel approach for episode mining in a data stream. Our method distinguishes itself by scanning newly added data to update existing frequent episodes, all without the need for scanning the original data or searching for candidate episodes. The experiments also show that our approach is more efficient compared to other existing methods. © 2024 IEEE.

关键词： Data mining

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Reconnaissance Detection in Decentralised IoT System using Model-Contrastive Federated Learning 18

Reconnaissance Detection in Decentralised IoT System using M...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Dutta, Rajdeep Kumar Chhetry, Bishal Matam, Rakesh Barbhuiya, Ferdous Ahmed Indian Institute of Information Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350391725

The detection of reconnaissance attacks is crucial for safeguarding Internet of Things (IoT) environments, which are inherently more vulnerable and resource-constrained compared to traditional computing systems. Traditional centralised detection methods face significant challenges, such as privacy concerns and limited scalability due to the need to aggregate raw data on a central server. These issues become particularly pronounced in IoT environments where devices are diverse and geographically distributed. To address these challenges, we propose ReconGuard, a novel approach leveraging Model Contrastive Federated Learning (MCFL). ReconGuard enables collaborative training across multiple IoT devices without the necessity of sharing raw data, thereby preserving user privacy and enhancing data security. The approach integrates contrastive learning techniques, which improve the model's ability to discriminate between benign and malicious activities by contrasting positive (similar) and negative (dissimilar) data pairs. Our experimental results demonstrate that the ReconGuard-based detection system provides a scalable and privacy-preserving solution for identifying reconnaissance activities. These activities are often the initial step in more severe cyber threats, such as botnet attacks, which can lead to significant disruption and damage. By effectively detecting reconnaissance activities, ReconGuard enhances the overall cybersecurity framework of IoT environments. This research presents a viable and innovative method for improving cybersecurity in IoT systems, addressing the critical need for scalable, efficient, and privacy-preserving intrusion detection solutions. The use of MCFL in ReconGuard not only mitigates the challenges of data centralisation but also leverages the distributed nature of IoT networks to enhance robustness against cyber threats. © 2024 IEEE.

关键词： Contrastive Learning

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Intelligent Behavior Recognition based on Gyroscope and Accelerometer

Intelligent Behavior Recognition based on Gyroscope and Acce...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Huang, Pin-Yu Lu, Yung-Han Lin, Chun-Ting He, Bo-Xian Pai, Tun-Wen Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

In an aging society, the rate of dementia would be dramatically increased. It is urgent to provide intelligent facilitative devices and appropriate medical care for those who with dementia symptoms, especially for the elders. Accordingly, the primary research target of this study is to automatically detect and monitor behavior of the elderly suffered from dementia. Although previous research has been conducted to identify various human movement patterns, the experimental data were all received from cameras. Furthermore, these related applications are not specifically designed to identify the behavior of the elderly with dementia. In order to improve previous research methods, our study adopted wearable multi-axis sensors to collect corresponding motion data with protected privacy. In addition to collecting raw data containing time series signals, frequency and spatial domain features were applied to detect human behaviors, and Random Forest algorithm was applied to construct a motion prediction model. The classification system for real-time motion recognition contained two main actions of users including holding/putting hand gestures and walking model. Finally, an additional camera on the chest was designed to be triggered and to take a shot according to previous settings. It could enhance practical applicability of the proposed intelligent system to assist the elderly with dementia to find her/his lost objects. This study developed an app for recording individual behaviors, checking the location and time of lost objects, and even recording activity information to provide healthcare professionals with decision-making suggestions for subsequent precision medical care. © 2023 IEEE.

关键词： Accelerometers

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A Highly Scalable Automated Universal Binary Malware Analysis Framework for Healthcare and Medical SaaS 24

A Highly Scalable Automated Universal Binary Malware Analysi...

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8th International Conference on Medical and Health Informatics, ICMHI 2024

作者： Cheng, Shin-Ming Fan, Chin-Chi Chien, Bo-Han Chang Chen, Chia-Yi Lin, Wei-Hung Liao, Chen-Chien Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei City106335 Taiwan Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei City106335 Taiwan

ISBN: (纸本)9798400716874

In recent years, there has been a growing emphasis on information security, with major companies introducing cybersecurity teams to ensure the safety of data. However, there is a lack of convenient channels for general users to engage in cybersecurity protection, leaving them with no choice but to rely on various antivirus software installations to safeguard their privacy and financial assets. This phenomenon is particularly evident in the digital transformation of healthcare and medical information systems, where the substantial amount of digitized patient data has become a crucial asset within hospital systems. Unfortunately, it has also made healthcare systems potential targets for cyberattacks. The databases of major hospitals have become vulnerable to malicious virus invasions, posing significant threats to patient privacy and the operational integrity of healthcare institutions. Faced with such threats, there is an increasing need for comprehensive cybersecurity protection mechanisms. To address this issue, we have developed the Universal Binary Malware Analysis Framework (UBMAF), an easily accessible binary file analysis framework for the general public. UBMAF integrates multiple open-source static and dynamic analysis tools into an automated module, deployed as Software as a Service (SaaS) in the cloud for healthcare and medical systems. This eliminates the need for users to install applications, and the framework interface is optimized for intuitive usability. During the usage process, users can freely choose module combinations. After uploading files to UBMAF, the framework conducts corresponding tool analyses or file processing based on the selected modules. Ultimately, it provides users with downloadable results and analysis reports. This design enables large healthcare and medical systems to quickly and conveniently enhance their cybersecurity defenses while ensuring the security of digital medical data, effectively addressing the challenges brought abo

关键词： Cybersecurity

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DMDA: A Computational Resource Allocation Approach for IoT Devices in Fog Computing 4

DMDA: A Computational Resource Allocation Approach for IoT D...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Pramono, Luthfan Hadi Shen, Shan-Hsiang National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798331506490

Fog Computing enables the efficient offloading of computational tasks from IoT Devices to Fog nodes, enhancing processing efficiency and reducing latency. This paper introduces the Dynamic Matching with Deferred Acceptance (DMDA) algorithm, designed to optimize the allocation of GPU and CPU resources within Fog Computing environments, specifically for smart city applications. The DMDA algorithm allocates resources to IoT Devices based on parameters such as data type, computational load, priority, and responsiveness. Devices processing image or video data are given priority for GPU allocation, while other devices are assigned to CPUs based on their cumulative performance scores. Devices with lower priority are reassigned to CPU resources or excluded when resource availability is exhausted, significantly enhancing performance under high-demand scenarios. The algorithm employs a Mixed-Integer Linear Programming (MILP) model to maximize the aggregate score of allocated devices while adhering to resource constraints. Experiments conducted with 50 to 1,000 devices demonstrate that both the total Device Score rate and Allocated Device rate reach 100%, indicating optimal allocation. Empirical findings show that the DMDA algorithm improves resource utilization, reduces processing times, and ensures effective workload distribution across large-scale IoT deployments. © 2024 IEEE.

关键词： Fog computing

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