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Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

computers, Materials & Continua 2023年第2期74卷 2379-2395页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Electrocardiogram(ECG)signal is a measure of the heart’s electrical ***,ECG detection and classification have benefited from the use of computer-aided systems by *** goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification *** addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall *** prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing ***,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA *** results confirmed the superiority and effectiveness of the proposed *** classification accuracy achieved by the proposed approach is(99.98%).

关键词： Electrocardiogram differential evolution algorithm dipper throated optimization neural networks

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Proposed intelligence systems based on digital Forensics: Review paper

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Materials Today: Proceedings 2023年 80卷 2647-2651页

作者： Ismael Sahib, Hussein Qahatan AlSudani, Mustafa Hasan Ali, Mohammed Qassim Abbas, Haydar Moorthy, Kohbalan Mundher Adnan, Myasar Faculty of Computer Science and Information Technology UniversitiTun Hussein Onn Malaysia Computer Techniques Engineering Department Faculty of Information Technology Imam Ja'afar Al-sadiq University Najaf Iraq Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Kuantan Malaysia Islamic University Najaf Iraq Faculty of Engineering School of Computing Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia

The field of information security, in general, has seen shifts a traditional approach to an intelligence system. Moreover, an increasing of researchers to focus on propose intelligence systems and framework based on the forensic case studies because of the limitations of traditional methods such as analysis intensive data manually, intelligence visualization to make the evidence more understandable and intelligence system for store data. However, most of these intelligence systems are still facing different limitations. Furthermore, the primary goal of this work analysis popular intelligence system that was used based on forensic. Moreover, propose new algorithms and hybrid model which it's achieved good results in dif-ferent other fields to develop the forensic systems in the future. © 2021

关键词： Digital forensics

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Improving the review classification of Google apps using combined feature embedding and deep convolutional neural network model

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4257-4272页

作者： Aslam, Naila Alzamzami, Ohoud Xia, Kewen Sadiq, Saima Umer, Muhammad Bisogni, Carmen Ashraf, Imran School of Electronics and Information Engineering Hebei University of Technology Tianjin China Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Department of Computer Science Information Technology The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Salerno Fisciano Italy Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of

Online reviews play an integral part in making mobile applications stand out from the large number of applications available on the Google Play store. Predominantly, users consider posted reviews for appropriate app selection. Manual categorization of such reviews is both inefficient and time-consuming. Therefore, automatic analysis of the sentiments of such reviews provides fast suggestions for new users and facilitates their selection of the appropriate app. However, data imbalance is a major challenge for performing class prediction of such reviews as their distribution is sparse and often leads to low accuracy. This work proposes a framework to overcome this limitation. Extensive experiments are performed using the original and balanced data with the synthetic minority oversampling technique (SMOTE) and adaptive synthetic sampling (ADASYN). Additionally, deep learning and machine learning models are evaluated using FastText, FastText Subword, global vector (GloVe), and their combinations for word representation. Baseline machine learning models, including random forest, extra tree classifier, gradient boosting, Naive Bayes, logistic regression (LR), stochastic gradient descent (SGD), and voting classifier (VC) that combines LR and SGD, are used for comparison. The outcomes show that the convolutional neural network using a combination of word embedding techniques produces the most accurate results. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolutional neural networks

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Safeguarding Fuel Resources for BTS Generator Sets: An Arduino-Based Monitoring System with Fuel Theft Detection Prototype 2

Safeguarding Fuel Resources for BTS Generator Sets: An Ardui...

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2nd International Conference on information technology Research and Innovation, ICITRI 2023

作者： Erlina, Tati Aisuwarya, Ratna Alwizar Universitas Andalas Computer Engineering Faculty of Information Technology Padang Indonesia

ISBN: (纸本)9798350324945

Fuel theft from Base Transceiver Station (BTS) generator sets (Gensets) is a prevalent issue that poses financial losses and operational disruptions for telecommunication companies. Traditional security measures have limitations in preventing fuel theft effectively, necessitating innovative monitoring systems. This paper presents the design and testing of a monitoring system with a fuel theft detection prototype for Gensets at BTS. The results obtained demonstrate promising findings. The fuel sensor (float sensor) accurately detects fuel volume reductions with an average error of 1.16% compared to manual and system measurements and reliably detects fuel volume increases with an average error of 1.62%. The SCT-103 current sensor successfully detects the operational status of the Gensets with a 100% success rate by measuring the current flowing to the load. Using internet connectivity, the NodeMCU ESP32 enables seamless data transmission and storage to databases like Firebase and Google Sheets. The developed smartphone application provides real-time information on available fuel volume and user-friendly access to historical fuel usage data. These findings highlight the potential and feasibility of the prototype system as an effective and efficient solution for monitoring fuel volume in Gensets at BTS, with potential applications in various industries. Further research and implementation can be pursued to optimize and expand the system's capabilities for practical use in real-world scenarios. © 2023 IEEE.

关键词： Digital storage

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Analysis of energy consumption for SPI and I2C communications in ultra-low power embedded systems 46

Analysis of energy consumption for SPI and I2C communication...

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46th ICT and Electronics Convention, MIPRO 2023

作者： Zidar, Josip Aleksi, Ivan Matic, Tomislav Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

ISBN: (纸本)9789532331042

Developing ultra-low-power devices requires low-power components, algorithms, and communication protocols. For environmental monitoring along the supply chain, products may travel a long way from the distributor to the customer. In addition, extreme climatic circumstances, such as temperatures below 0°C, affect battery capacity. In order to monitor package's environment, it is crucial to ensure that all embedded system parts use as little energy as possible. This paper addresses how much power the SPI and I2C communication protocols use. These protocols are widely used in low-power embedded devices for sensor communication. Based on its results, this research article presented recommendations for developing ultra-low power embedded devices that use the SPI/I2C protocol. © 2023 MIPRO Croatian Society.

关键词： Supply chains

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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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An Improved Hybrid Deep Learning Approach for Security Requirements Classification

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computers, Materials & Continua 2025年第3期82卷 4041-4067页

作者： Shoaib Hassan QianmuLi Muhammad Zubair Rakan AAlsowail Muhammad Umair School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210094China Faculty of Information Technology and Computer Science University of Central PunjabLahore5400Pakistan Computer Skills Self-Development Skills DevelopmentDeanship of Common First YearKing Saud UniversityRiyadh11362Saudia Arabia

As the trend to use the latestmachine learning models to automate requirements engineering processes continues,security requirements classification is tuning into the most researched field in the software engineering *** literature studies have proposed numerousmodels for the classification of security ***,adopting those models is constrained due to the lack of essential datasets permitting the repetition and generalization of studies employing more advanced machine learning ***,most of the researchers focus only on the classification of requirements with security *** did not consider other nonfunctional requirements(NFR)directly or indirectly related to *** has been identified as a significant research gap in security requirements *** major objective of this study is to propose a security requirements classification model that categorizes security and other relevant security *** use PROMISE_exp and DOSSPRE,the two most commonly used datasets in the software engineering *** proposed methodology consists of two *** the first step,we analyze all the nonfunctional requirements and their relation with security *** found 10 NFRs that have a strong relationship with security *** the second step,we categorize those NFRs in the security requirements *** proposedmethodology is a hybridmodel based on the ConvolutionalNeural Network(CNN)and Extreme Gradient Boosting(XGBoost)***,we evaluate the model by updating the requirement type column with a binary classification column in the dataset to classify the requirements into security and non-security *** performance is evaluated using four metrics:recall,precision,accuracy,and F1 Score with 20 and 28 epochs number and batch size of 32 for PROMISE_exp and DOSSPRE datasets and achieved 87.3%and 85.3%accuracy,*** proposed study shows an enhancement in metrics

关键词： Requirements engineering security requirements deep learning CNN XGBoost classification

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Helmet Detection Recognition Algorithm Based on Improved Yolov5

Helmet Detection Recognition Algorithm Based on Improved Yol...

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2023 International Conference on Image Processing and computer Vision, IPCV 2023

作者： Lin, Shuo Li, Zongmei Xiamen University of Technology Faculty of Computer and Information Engineering Xiamen China

ISBN: (纸本)9781665473941

The existing helmet detection algorithm is mainly based on a single-stage object detection algorithm, which has high detection speed and can achieve the requirement of real-time detection. Still, the accuracy of detecting small objects and objects with obstacles is no high. Based on this, this paper proposes a helmet detection recognition algorithm based on improved YOLOv5. The SE attention mechanism has the advantages of low complexity, few parameters and low computational effort. Firstly, the SE attention module is added to the Yolov5 backbone network to weigh the features of different channels and improve the model's attention to important features. Secondly, using softnms to replace nms in the original network can retain more small target prediction frames, improve the detection accuracy, and improve the model's ability to detect small targets. The experiments show that the average accuracy, precision, and recall of the proposed method reach 96.51%, 95.20%, and 90.08%, respectively, and the real-time performance meets the need for helmet detection in practical work. © 2023 IEEE.

关键词： Object detection

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A routing method with the approach of reducing energy consumption in WSNs with the Jellyfish Search (JS) optimizer algorithm and unequal clustering 13

A routing method with the approach of reducing energy consum...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Gholami, Ehsan Hamidzadeh, Javad Sadjad University Faculty of Computer Engineering and Information Technology Mashhad Iran

ISBN: (纸本)9798350330151

Wireless sensor networks are among the most common communication tools used in military and civilian fields. These networks consist of many tiny devices called sensor nodes. Each node has processing capability and different types of memory, transmitter, radio frequency receiver, and power supply. In addition, nodes house sensors and actuators. Batteries usually power sensor nodes and need to operate longer without their presence. In most cases, changing or recharging the battery for sensor nodes is very difficult or even impossible. Designing routing protocols in wireless sensor networks is challenging due to network limitations, with an emphasis on energy efficiency. The proposed method uses a hierarchical routing mechanism to optimize routing using asymmetric clustering. The sensor nodes are asymmetrically clustered in the proposed method, and the cluster heads are selected using the Jellyfish Search (JS) optimizer algorithm. The problem of optimal clustering is an optimization problem, so in this research, the Jellyfish Search algorithm is used to provide the most optimal clusters in routing. Due to the utilization of a search mechanism based on chaos theory in the Jellyfish Search optimization, the proposed method exhibits high accuracy in clustering and optimal routing within wireless sensor networks. Evaluations indicate that the proposed method achieves lower energy consumption and longer network lifetime compared to GWO, MO-PSO, WGWO, and HMBCR algorithms. © 2023 IEEE.

关键词： Energy utilization

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Prevalent Frequency of Emotional and Physical Symptoms in Social Anxiety using Zero Shot Classification: An Observational Study 9

Prevalent Frequency of Emotional and Physical Symptoms in So...

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9th Workshop on Computational Linguistics and Clinical Psychology, CLPsych 2024

作者： Rizwan, Muhammad Demšar, Jure Department of Information Technology Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan Faculty of Computer and Information Studies University of Ljabljana Slovenia

ISBN: (纸本)9798891760806

Social anxiety represents a prevalent challenge in modern society, affecting individuals across personal and professional spheres. Left unaddressed, this condition can yield substantial negative consequences, impacting social interactions and performance. Further understanding its diverse physical and emotional symptoms becomes pivotal for comprehensive diagnosis and tailored therapeutic interventions. This study analyze prevalence and frequency of social anxiety symptoms taken from Mayo Clinic, exploring diverse human experiences from utilizing a large Reddit dataset dedicated to this issue. Leveraging these platforms, the research aims to extract insights and examine a spectrum of physical and emotional symptoms linked to social anxiety disorder. Upholding ethical considerations, the study maintains strict user anonymity within the dataset. By employing a novel approach, the research utilizes BART-based multi-label zero-shot classification to identify and measure symptom prevalence and significance in the form of probability score for each symptom under consideration. Results uncover distinctive patterns: "Trembling" emerges as a prevalent physical symptom, while emotional symptoms like "Fear of being judged negatively" exhibit high frequencies. These findings offer insights into the multifaceted nature of social anxiety, aiding clinical practices and interventions tailored to its diverse expressions. ©2024 Association for Computational Linguistics.

关键词： Large datasets

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