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International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Adel Mahmoud Samuel Medhat Mark Maged Othman Mohamed Reham Karam Khaled Salah M. Watheq El-Kharashi Department of computer and systems Faculty of engineering Ain Shams University Cairo Egypt Siemens Digital Industries Software Fremont USA

ISBN: (纸本)9781665482387

Data compression is a trending field that is used in data storage and data transmission systems. Lossy compression means that data cannot be completely retrieved while in lossless compression the compressed data must be reconstructed exactly. Lossless data compression is used in compressing binary files, telemetry data and high-fidelity medical and scientific images where details are crucial. There is no generic compression algorithm that gives best compression ratio on all data pattern. In this paper, we propose a hybrid lossless hardware architecture that compresses most of data patterns such as repeated data, Gaussian distribution data and images. A profiling-before-compressing and then choosing the right compression hardware is proposed. The proposed design is a highly parallelized architecture that can compress/decompress 64 bytes/cycle with minor overhead. Moreover, it provides high compression ratio on small block sizes as well as large ones.

关键词： Image coding Data compression Signal processing algorithms Memory Signal processing Predictive coding Prediction algorithms

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QSroute : A QoS Aware Routing Scheme for software Defined Networking

QSroute : A QoS Aware Routing Scheme for Software Defined Ne...

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International Conference on software engineering and computer systems (ICSECS)

作者： Imran Edzereiq Kamarudin Mohamed Ariff Ameedeen Muhamad Idaham Umar Ong Azlee Zabidi Department of Computer Systems and Networking Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia Department of Software Engineering Faculty of Computing Universiti Malaysia Pahang AL-Sultan Abdullah Pekan Malaysia

The increasing demand for bandwidth-intensive network applications, such as video streaming, multimedia, and Internet of Things (IoT) applications, necessitates improved resource management to protect the network without compromising Quality of Service (QoS). Meeting these challenges requires a centralized view of all available network resources. software Defined Networking (SDN), an emerging technology, provides a centralized view and control of network resources. This feature enables administrators to programmatically define and manage network behavior, including routing, making it more flexible and adaptable. This study proposes a QoS-aware routing scheme for SDN that considers available bandwidth, packet delay, and packet loss to determine the optimal routing path. Optimal paths are selected based on meeting predefined threshold criteria. The study concludes by discussing potential directions for future research in this field.

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Intelligent Gender Recognition System for Classification of Gender in Malaysian Demographic 1

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5th International Conference on Electrical, Control and computer engineering, InECCE 2019

作者： Chi, Yap Su Kamarulzaman, Syafiq Fauzi Faculty of Computer Systems & Software Engineering University of Malaysia Pahang Kuantan Malaysia

ISBN: (数字)9789811523175

ISBN: (纸本)9789811523168

Identification of a person gender as a man or woman based on the past experiences through features of face such as eyes, mouth, cheek can be obtained through an intelligent gender recognition system. Detection of a person’s gender can be difficult but important for security purposes, especially where safety issues concerning woman in public amenities. The objectives of this research are to identify the techniques for classifying features from man and woman facial images, through which embed as a system and validify using photos within Malaysian demographic. This research is focused on utilizing facial features for gender classification in real time, emphasizing on deep learning-based gender recognition and HAAR Cascade classifier using pre-trained caffe model in OpenCV library. Results show that under Malaysian demographic, probability of 86% accuracy of gender recognition were obtained. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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Named numeric characteristics extraction from text data in Russian 11

Named numeric characteristics extraction from text data in R...

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11th Majorov International Conference on software engineering and computer systems, MICSECS 2019

作者： Uskov, Ivan Yarkeev, Alexander Tsopa, Evgenij Faculty of Software Engineering and Computer Systems ITMO University Saint Petersburg Russia

The article is focused on the problem of named numeric characteristics extraction from the text data in Russian. This problem have a significant impact to the wide variety of natural language processing tasks such as marketing analysis, statistic tools and data aggregation solutions. There are a lot of existing approaches such as scrappers and parsers, various natural language processing tools (Stanford CoreNLP, spaCy, Natural language toolkit, Apache OpenNLP) and Tomita parser from Yandex that are considered in the article. The structure of the numerical data in Russian has its own specific that have an impact to the algorithms of converting numbers from text form to their value that is also covered in the article. As a result of the research, the new method for extracting numerical data from texts in a natural language was proposed and software module for the proposed method proof was developed. The proposed solution uses semantic networks and semantic frames to determine the boundaries and extract the numerical data from the text. The developed software module was tested on a variety of data sets extracted from the different sources such as Avito and ***. The results of the testing shows the effectiveness of the proposed method in comparision with existing solutions. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： software testing

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare 7

Artificial Intelligence-based Service Chains Scheduling for ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Soni, Mukesh Rusho, Maher Ali Byeon, Haewon Alghamdi, Azzah Hannan, Shaikh Abdul Dave, Parth Rameshchandra Dr. D. Y. Patil School of Science & Technology Tathawade Dr. D. Y. Patil Vidyapeeth Marashtra Pune India University of Colorado Department of Lockheed Martin Engineering Management BoulderCO80309 United States Inje University Department of Ai and Software & Medical Big Data Research Centre Gimhae50834 Korea Republic of Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia AlBaha University Faculty of Computing and Information AlBaha Saudi Arabia L. D. College of Engineering Department of Computer Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350350067

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare. © 2024 IEEE.

关键词： Electronic health record

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Blockchain-Based Decentralized Breast Cancer Patient Monitoring System for Remote Healthcare Security

Blockchain-Based Decentralized Breast Cancer Patient Monitor...

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Advancements in Computing (ICAC), International Conference on

作者： K.N. Dilshan H.A.C.K. Rathnayake L.P.T.L. Pathirana D.H.S.V. Dhanapala Harinda Fernando Pradeep K.W Abeygunawardhana Faculty of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

Breast cancer is a disease that is prevalent all over the world, and that is essential for the efficient treatment and care of breast cancer patients who are monitored remotely. However, traditional conventional centralized systems for patient monitoring face challenges in providing effective and efficient care for breast cancer patients, including limited access to medical records, ineffective communication, and patient participation. Fragmentation of records and difficulty in obtaining information can hinder patient care in terms of both privacy and data security. The proposed system uses machine learning, blockchain, smart contracts, hybrid access control, and hybrid intrusion prevention and detection to diagnose breast cancer decentralized. The technology analyses medical data using advanced machine learning algorithms to diagnose breast cancer accurately and provide healthcare practitioners with relevant insights. The blockchain stores patient information, diagnostic results, and metadata transparently and securely to protect data integrity and privacy. Smart contracts automate and enforce rules, securing data and improving diagnostic workflow. User roles and attributes are used to manage access to sensitive data and improve collaboration. Patients, doctors, and researchers have different permits and limits. Secures granular data and prevents unauthorized access. Scalability and network congestion are addressed via a hybrid network management system. The system optimizes resource allocation, load balancing, and data transfer by integrating centralized and decentralized methodologies. These features reduce delays and assure seamless operation. Experimental results reveal that the suggested system detects breast cancer more accurately than centralized methods. Blockchain, smart contracts, and access control ensure data integrity, privacy, and over-security. Scalability and reactivity are improved by the hybrid network management system, making breast cancer detec

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Smart room for patient monitoring based on IoT technologies 23

Smart room for patient monitoring based on IoT technologies

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Proceedings of the 2023 6th Artificial Intelligence and Cloud Computing Conference

作者： Nataly Zhukova Anna Motienko Dmitriy Levonevskiy Alexey Subbotin Pham Tuan Anh Laboratory of big data technologies in socio-cyberphysical systems St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) Russia Faculty of Computer Technologies and Informatics St. Petersburg State Electrotechnical University Russia Faculty of Software Engineering and Computer Systems ITMO University Russia

ISBN: (纸本)9798400716225

This article presents a method for building a hospital room with extended intelligent features based on Internet of Things (IoT) technologies. We begin by reviewing existing vision-based health care systems and identifying important and useful functions that they should have. We then present a method for building a smart room that allows increase the level of the intelligence of the existing hospital rooms due to extensive use of the up-to-date machine learning methods. The proposed solution assumes using readily available hardware devices to reduce the cost of smart room building. Compared to commercially available assistive technologies, the proposed smart room turn out to have high level of intelligence and to be very cost effective. The system's non-intrusive nature makes it easy to use it both in hospitals and at home for patient care.

关键词： Internet of Things IoT automation in medicine intellectual room machine learning smart room smart ward

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Time-Series Anomaly Detection of Mozi Malware in IoT Devices Using Arima and Local Outlier Factor

Time-Series Anomaly Detection of Mozi Malware in IoT Devices...

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International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Tünde Kaufman Jozsef Katona National Media and Infocommunications Authority Budapest Hungary Kandó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary

ISBN: (数字)9798331515478

ISBN: (纸本)9798331515485

The increasing demand for IoT devices in industries has generated a high level of cybersecurity threats, with botnets including Mozi exploiting poor passwords and unpatched vulnerabilities to target networked infrastructure. Peer-to-peer (P2P) topology of Mozi, employing Distributed Hash Tables (DHTs) for Command-and-Control (C2) communications, makes it resistant to takedown operations and capable of high-scale Distributed Denial-of-Service (DDoS) attack, data exfiltration, and remote command execution. Traditional one-class, machine learning techniques such as OneClass Support Vector Machine (OCSVM) and Local Outlier Factor (LOF) have been widely used for discovering malwarerelated anomalies. However, such techniques require high labelled training datasets, whose availability in dynamically changing environments of malwares proves challenging. In this work, an AutoRegressive Integrated Moving Average (ARIMA)-based mechanism for discovering time-series anomalies in IoT-infected devices with Mozi infection is proposed. In an experimental setup using a Raspberry Pi running Pi-hole, baseline profiles of CPU consumption, RAM usage, and network activity are first established under normal conditions. Next, a system is subjected to a simulation of infection with Mozi malware and monitored for deviation with ARIMA forecasting and Local Outlier Factor (LOF) for discovering anomalies. By comparing predicted values with actual values, effective intrusion detection for malware-related anomalies is attained, providing a real-time, adaptive mechanism for early intrusion detection. In conclusion, with its effectiveness in discovering stealthy behaviour of malwares, employing ARIMA for discovering time-series anomalies in IoTinfected devices proves effective, and its employment in IoT cybersecurity aids in providing real-time mechanism for discovering threats for system admins and security professionals. As future work, a combination of ARIMA with deep neural networks such as Long

关键词： Training Botnet Intrusion detection Malware Real-time systems Peer-to-peer computing Topology Internet of Things Long short term memory Anomaly detection

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Development of a Model for Discriminating Electroencephalograms of Patients with Mild Cognitive Impairment and Healthy Individuals Using a Support Vector Machine 13

Development of a Model for Discriminating Electroencephalogr...

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Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Tanabe, Akifumi Nakatani, Kyosuke Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Kamae-Shigenobu, Kazue Shinosaki, Kazuhiro Graduate School of Information Science University of Hyogo Kobe Japan School of Life Science and Technology Tokyo Institute of Technology Yokohama Japan Aino University Department of Medical Engineering Ibaraki Japan Institute for Datability Science Osaka University Suita Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Aomori University Faculty of Software and Information Technology Aomori Japan Osaka University Cyber Media Center Suita Japan Asakayama General Hospital Sakai Japan

ISBN: (纸本)9798350373332

We performed machine learning using spectrum analysis and power spectra of electroencephalograms (EEGs) of patients with mild cognitive impairment (MCI). Spectrum analysis was performed on the EEGs of MCI patients and healthy individuals, and the power spectrum values of the two groups were calculated and compared. Based on the power spectrum values of the theta and alpha bands, models for discriminating MCI patients were created using a support vector machine (SVM), and their accuracies were compared. In the spectrum analysis, the theta band showed significantly higher power spectrum values in the MCI group. In the comparison of the accuracies of SVM models, the model using the theta band showed higher values in parameters, such as accuracy rate. These results suggested that the power spectrum values of the theta band are useful for discriminating MCI patients. © 2024 IEEE.

关键词： Electroencephalography

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Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

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