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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

Computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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Power Consumption and Prototype Evaluation of IoT Devices for Environmental Monitoring Systems 22

Power Consumption and Prototype Evaluation of IoT Devices fo...

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22nd International Symposium on Communications and information Technologies, ISCIT 2023

作者： Hirai, Kota Tsutsui, Hiroshi He, Ying Ohgane, Takeo Hokkaido University Graduate School of Information Science and Technology Japan Hokkaido University Faculty of Information Science and Technology Japan University of Technology Sydney School of Electrical and Data Engineering Australia

ISBN: (纸本)9781665457316

In this paper, we conduct experiments and evaluations of intermittent operation with the goal of reducing power consumption in IoT devices for environmental monitoring systems. This intermittent operation is realized by alternately transitioning the CPU between its normal operating states and a deep sleep mode with minimal power consumption. While increasing the sensing interval can reduce power consumption, it also increases the risk of data loss, thus presenting a tradeoff. This paper evaluates the reduction in power consumption by integrating three methods: (1) disconnecting power supply to modules not in use, (2) utilizing CPU's low-power mode, and (3) minimizing power consumption through intermittent operation. From the experimental results, the ratio of static to dynamic power was calculated. The methods (1) and (2) are used to reduce static power consumption to the maximum extent, while for dynamic power, we demonstrate the relationship between the interval of intermittent operation, power consumption, and operating time. In this paper, we also show a result of long-term environmental monitoring experiment with our developed IoT devices. © 2023 IEEE.

关键词： Electric power utilization

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Generalizing Human Motion Style Transfer Method Based on Metadata-independent Learning 24

Generalizing Human Motion Style Transfer Method Based on Met...

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2024 SIGGRAPH Asia 2024 Posters, SA 2024

作者： Era, Yuki Togo, Ren Maeda, Keiske Ogawa, Takahiro Haseyama, Miki Graduate School of Information Science and Technology Hokkaido University Hokkaido Japan Faculty of Information Science and Technology Hokkaido University Hokkaido Japan Data-Driven Interdisciplinary Research Emergence Department Hokkaido University Hokkaido Japan

ISBN: (纸本)9798400711381

This study aims to extend the applicability of motion style transfer methods to be robust for diverse and complex motions akin to those found in real-world data. We introduce a new training method without style labels which are metadata needed by conventional motion style transfer methods and enable a model to learn from almost motion datasets. This contribution mitigates performance degradation for motions absent in style-motion datasets. Compared to conventional methods, the proposed method demonstrates superior transfer performance, particularly under conditions featuring more diverse and complex motions. © 2024 Copyright held by the owner/author(s).

关键词： Transfer learning

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Phishing Attacks Detection Using EnsembleMachine Learning Algorithms

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Computers, Materials & Continua 2024年第7期80卷 1325-1345页

作者： Nisreen Innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Marwan Abu-Zanona Bassam Mohammad Elzaghmouri Farah H.Zawaideh Mouiad Fadeil Alawneh Department of Computer Science and Information Systems College of Applied SciencesAlmaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26110Jordan Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjloun26767Jordan

Phishing,an Internet fraudwhere individuals are deceived into revealing critical personal and account information,poses a significant risk to both consumers and web-based *** indicates a persistent rise in phishing ***,these fraudulent schemes are progressively becoming more intricate,thereby rendering them more challenging to ***,it is imperative to utilize sophisticated algorithms to address this *** learning is a highly effective approach for identifying and uncovering these harmful *** learning(ML)approaches can identify common characteristics in most phishing *** this paper,we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing *** that,we used the normalization technique on the dataset to transform the range of all the features into the same *** findings of this paper for all algorithms are as follows in the first dataset based on accuracy,precision,recall,and F1-score,respectively:Decision Tree(DT)(0.964,0.961,0.976,0.968),Random Forest(RF)(0.970,0.964,0.984,0.974),Gradient Boosting(GB)(0.960,0.959,0.971,0.965),XGBoost(XGB)(0.973,0.976,0.976,0.976),AdaBoost(0.934,0.934,0.950,0.942),Multi Layer Perceptron(MLP)(0.970,0.971,0.976,0.974)and Voting(0.978,0.975,0.987,0.981).So,the Voting classifier gave the best *** in the second dataset,all the algorithms gave the same results in four evaluation metrics,which indicates that each of them can effectively accomplish the prediction ***,this approach outperformed the previous work in detecting phishing websites with high accuracy,a lower false negative rate,a shorter prediction time,and a lower false positive rate.

关键词： Social engineering attacks phishing attacks machine learning security artificial intelligence

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Smart Agriculture: Enhancing Crop Management through IoT-Based Real-Time Monitoring and Automation 9

Smart Agriculture: Enhancing Crop Management through IoT-Bas...

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9th International Conference on information technology and Digital Applications, ICITDA 2024

作者： Paliyanny, Hushalictmy Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Nalluri, Madhusudana Rao Cholla, Ravindra Raman Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India Icfai Foundation for Higher Education Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331510732

Internet of Things plays an important role in agriculture in order to provide an innovative and smart solution to traditional farming. IOT is all about connecting physical devices to the internet and can access from a remote place. So that crop monitoring becomes easy even if the farmer is in a remote place. Due to automation, human efforts are required less. Also the IoT data helps the farmer make decisions about planting, harvesting or fertilizing. This paper explains the system we have implemented for monitoring the agriculture with the help of IoT. The system is developed in order to provide real time information on crop conditions to the farmer. For that, it uses various types of sensors connected to the microcontroller in order to measure parameters like temperature, soil moisture, and water level, and accordingly, it activates actuators like a fan, motor or pump to manage ventilation and irrigation. We have used the GSM module to send the alert to the farmer and the WiFi module is used to send the agriculture data to the cloud platform. So that farmer can monitor the farm or crops remotely. The system is continuously collecting farm data from the sensors. If the crop soil is very dry them system will turn on the water pump. Also, for high temperature, the fan will get activated in order to cool down that area. This feature is mostly used in indoor farming. The farmer can monitor the farm condition in real time with the help of cloud platform. This system is helpful for improving productivity, crop management, and save resources i.e. efficient use of water which prevents water wastage. © 2024 IEEE.

关键词： Soil moisture

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Applying Text Mining to Analyze Human Question Asking in Creativity Research 12th

Applying Text Mining to Analyze Human Question Asking in C...

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12th International Conference on Big data Analytics, BDA 2024

作者： Wròblewska, Anna Korbin, Marceli Kenett, Yoed N. Dan, Daniel Ganzha, Maria Paprzycki, Marcin Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland Faculty of Data and Decision Sciences Technion - Israel Institute of Technology Haifa Israel School of Applied Data Science Modul University Vienna Vienna Austria Systems Research Institute Polish Academy of Sciences Warsaw Poland

ISBN: (纸本)9783031861925

Creativity relates to the ability to generate novel, and effective, ideas in the areas of interest. How are such creative ideas generated? One possible mechanism that supports creative ideation, and is gaining increased empirical attention, is by asking questions. Question asking is a likely cognitive mechanism that allows defining problems, facilitating creative problem solving [15]. However, much is unknown about the exact role of questions in creativity. This work presents an attempt to apply text mining methods to measure the cognitive potential of questions, taking into account, among others, (a) question type, (b) question complexity, and (c) content of the answer. This contribution summarizes the history of question mining as a part of creativity research, along with the natural language processing methods deemed useful, or helpful, in the study. Furthermore, a novel approach is proposed, implemented, and applied to five datasets. Obtained experimental results are comprehensively analyzed, suggesting that natural language processing has a role to play in the creativity research. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Creativity Measuring questions Natural language processing Question asking

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Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems

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Computers, Materials & Continua 2024年第8期80卷 2721-2741页

作者： Tareq Hamadneh Khalid Kaabneh Ibraheem AbuFalahah Gulnara Bektemyssova Galymzhan Shaikemelev Dauren Umutkulov Sayan Omarov Zeinab Monrazeri Frank Werner Mohammad Dehghani Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Faculty of Information Technology Al-Ahliyya Amman UniversityAmman19328Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Department of Big Data Analytics and Data Science Software Development Company《QazCode》Almaty050063Kazakhstan Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany

This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization(MFO),inspired by the unique behaviors observed in magnificent frigatebirds in their natural *** foundation of MFO is based on the kleptoparasitic behavior of these birds,where they steal prey from other *** this process,a magnificent frigatebird targets a food-carrying seabird,aggressively pecking at it until the seabird drops its *** frigatebird then swiftly dives to capture the abandoned prey before it falls into the *** theoretical framework of MFO is thoroughly detailed and mathematically represented,mimicking the frigatebird’s kleptoparasitic behavior in two distinct phases:exploration and *** the exploration phase,the algorithm searches for new potential solutions across a broad area,akin to the frigatebird scouting for vulnerable *** the exploitation phase,the algorithm fine-tunes the solutions,similar to the frigatebird focusing on a single target to secure its *** evaluate MFO’s performance,the algorithm is tested on twenty-three standard benchmark functions,including unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** results from these evaluations highlight MFO’s proficiency in balancing exploration and exploitation throughout the optimization *** studies with twelve well-known metaheuristic algo-rithms demonstrate that MFO consistently achieves superior optimization results,outperforming its competitors across various *** addition,the implementation of MFO on four engineering design problems shows the effectiveness of the proposed approach in handling real-world applications,thereby validating its practical utility and robustness.

关键词： Optimization metaheuristic magnificent frigatebirds exploration exploitation

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Design of a Smart Home Door Security System with Face Detection and Smart Bell using ESP32-CAM 6

Design of a Smart Home Door Security System with Face Detect...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Salsabila, Nurhusna Siswanto, Apri Bayuaji, Luhur Informatics Department Faculty of Engineering Universitas Islam Riau Pekanbaru Indonesia Fac. Data Science and Information Technology INTI International University Malaysia

ISBN: (纸本)9798331522667

This research introduces the design and implementation of an advanced IoT-based home security system aimed at improving both convenience and safety for homeowners. The system integrates a smart doorbell with a mobile application, enabling real-time alerts for guest arrivals. Key features include facial recognition for automated door unlocking and notification of unrecognized visitors. When an unknown face is detected, the system sends an image along with a notification to the homeowner via a Telegram bot. The architecture incorporates the ESP32-CAM microcontroller for face detection, a solenoid lock for automatic door access, and an ultrasonic sensor to detect visitor presence. Extensive testing under various environmental conditions was conducted to assess the system's performance, particularly in terms of face recognition accuracy and notification delivery. The results indicate that the system significantly enhances home security by ensuring timely notifications and automating door access for verified individuals. While the system performs reliably in well-lit environments, challenges remain in low-light conditions. Software testing confirmed the robustness of the underlying logic. This innovative IoT solution overcomes the limitations of traditional security systems, offering a more secure, user-friendly, and efficient alternative for modern home security. © 2025 IEEE.

关键词： Ultrasonic sensors

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Detecting Community Through User Similarity Analysis on Twitter 18

Detecting Community Through User Similarity Analysis on Twit...

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18th International Conference on Ubiquitous information Management and Communication, IMCOM 2024

作者： Hasan, Md Ahsan Ul Bakar, Azuraliza Abu Yaakub, Mohd Ridzwan Universiti Kebangsaan Malaysia Sentiment Analysis Lab Center for Artificial Intelligence Technology Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Universiti Kebangsaan Malaysia Data Mining and Optimization Lab Center for Artificial Intelligence Technology Faculty of Information Science and Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350331011

Community detection in social relationship networks is a significant research domain within the field of social network analysis. However, Twitter sets itself apart from conventional friends' and followers' networks due to its users' distinct focus on sharing thoughts and ideas related to specific topics rather than prioritising personal connections. As a result, relying solely on friends' and followers' relations in Twitter networks for community detection would be insufficient and incomplete. In this paper, we proposed a Twitter community detection method that measures similarity using user profiles, tweet subjects, and tweet sentiment in this research. We constructed a similarity network using real-time Twitter API data and compared it to Zachary's Karate Network. The proposed approach enhances the quality function of modularity and conductance in the Louvain community detection algorithm, resulting in a more precise and meaningful community structure within the Twitter network. © 2024 IEEE.

关键词： Graph databases

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A Novel Hybrid Architecture for Superior IoT Threat Detection through Real IoT Environments

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Computers, Materials & Continua 2024年第11期81卷 2299-2316页

作者： Bassam Mohammad Elzaghmouri Yosef Hasan Fayez Jbara Said Elaiwat Nisreen Innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Farah H.Zawaideh Mouiad Fadeil Alawneh Asef Al-Khateeb Marwan Abu-Zanona Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26150Jordan Computer Engineering Department College of Engineering&Information Technology Buraydah CollegesBuraydah51418Saudi Arabia Faculty of Architecture and Design Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjlun26810Jordan Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia

As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense *** paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world *** proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive *** integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT *** evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated *** research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive *** model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security *** our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of *** model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat *** achievemen

关键词： A hybrid deep learning model IoT threat detection real IoT environments cybersecurity attention mechanism

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