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2023 International Conference on Informatics, Multimedia, Cyber and Information systems, ICIMCIS 2023

作者： Hartawan, Muhammad Syarif Maharani, Maya Dewi Dyah Krisnanik, Erly Rahman, Aedah Abd Krinadwipayana University Faculty of Engineering Jakarta Indonesia Sahid University Faculty of Environmental Engineering Jakarta Indonesia Universitas Pembangunan Nasional Veteran Jakarta Computer Science Faculty Departement Information System Jakarta Indonesia Asia e University Departement Information and Communication Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350307306

The use of information technology plays an important and necessary role in the development of ecotourism institutional. Likewise, if the development and progress of information technology can be applied to the small and medium businesses related to the ecotourism industry then, the selling value of the ecotourism industry will develop and increase. This research was conducted to determine the level of readiness of the ecotourism industry in carrying out the sustainable implementation of information technology in managing the ecotourism industry. We developed a research model by combining and adapting the technology readiness model and the successful information technology development model for the development of the ecotourism industry in six ecotourism units in Indonesia. This quantitative study involved 300 SME workers and managers. Data was processed and analyzed using the Multi Dimensional Scaling method, with descriptive data entered into a spreadsheet application. According to the analysis performed for this study, the sustainability index value was 53.11%, which is in the range of (50-74.99) percent or is fairly sustainable. The leverage attribute refers to the use of social media to promote wild products in the ecotourism sector. Wild products are goods or services that are produced without causing environmental harm. © 2023 IEEE.

关键词： Human resource management

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Preferences for Using Canva Apps as a Presentation Media Creation Tool Among Gen Z Students: SEM Analysis with TAM and UTAUT2 Approaches 25

Preferences for Using Canva Apps as a Presentation Media Cre...

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Maulana, Fairuz Iqbal Lestari, Dian Purnomo, Agung Arifuddin, Rahman Widartha, Vandha Pradwiyasma Adi, Puput Dani Prasetyo Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Medicine Airlangga Surabaya60132 Indonesia Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia Research Center for Telecommunication Bandung Indonesia

ISBN: (纸本)9798350378573

The purpose of this study is to find out what makes Generation Z students accept and use Canva as a tool for making presentation materials. The conceptual framework of this study is the combination of "Technology Acceptance Model (TAM)"and the "Unified Theory of Acceptance and Use of Technology (UTAUT2)". "Structural Equation Modeling (SEM)"used for data analyzation. This research aims to understand the dynamics of Canva acceptance among students. The findings show that enjoyment, fun, and pleasure in using Canva, combined with its creative tools and design-centric approach, significantly resonate with Gen Z's preference for aesthetics. This research highlights that Canva is favored by students for creating engaging presentations. © 2024 IEEE.

关键词： Students

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An Efficient Intrusion Detection Framework in Software-Defined Networking for Cybersecurity Applications

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computers, Materials & Continua 2022年第8期72卷 3529-3548页

作者： Ghalib H.Alshammri Amani K.Samha Ezz El-Din Hemdan Mohammed Amoon Walid El-Shafai Department of Computer Science Community CollegeKing Saud UniversityRiyadh28095Saudi Arabia Deanship of Scientific Research Saudi Electronic UniversityRiyadhSaudi Arabia Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh28095Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Electronics and Electrical Communications Eng.Depart. Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Network management and multimedia data mining techniques have a great interest in analyzing and improving the network traffic *** recent times,the most complex task in Software Defined Network(SDN)is security,which is based on a centralized,programmable ***,monitoring network traffic is significant for identifying and revealing intrusion abnormalities in the SDN ***,this paper provides an extensive analysis and investigation of the NSL-KDD dataset using five different clustering algorithms:K-means,Farthest First,Canopy,Density-based algorithm,and Exception-maximization(EM),using the Waikato Environment for Knowledge Analysis(WEKA)software to compare extensively between these five ***,this paper presents an SDN-based intrusion detection system using a deep learning(DL)model with the KDD(Knowledge Discovery in Databases)***,the utilized dataset is clustered into normal and four major attack categories via the clustering ***,a deep learning method is projected for building an efficient SDN-based intrusion detection *** results provide a comprehensive analysis and a flawless reasonable study of different kinds of attacks incorporated in the KDD ***,the outcomes reveal that the proposed deep learning method provides efficient intrusion detection performance compared to existing *** example,the proposed method achieves a detection accuracy of 94.21%for the examined dataset.

关键词： Deep neural network DL WEKA network traffic intrusion and anomaly detection SDN clustering and classification KDD dataset

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Algorithmic Trading Model for Stock Price Forecasting Integrating Forester with Golden Ratio Strategy 12

Algorithmic Trading Model for Stock Price Forecasting Integr...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Govindan, Niranjana Baskaran, Bharathi Azizan, Azizul Yakub, Fitri Dhamanti, Inge Dhaya, C. SRMIST Faculty of Engineering & Technology Department of Computing Technologies Kattankulathur Chennai India Faculty of Intelligence Kuala Lumpur Malaysia Malaysia-Japan International Institute of Technology UTM Electronic System Engineering Department Kuala Lumpur Malaysia Universitas Airlangga Faculty of Public Health Department of Health Policy and Administration Surabaya Indonesia Adhiparasakthi Engineering College Department of Computer Science and Technology Melmaruvathur India

ISBN: (纸本)9798350340204

Algorithmic trading has emerged as a powerful tool for making data-driven trading decisions in financial markets. In this paper, we present an innovative algorithmic trading model for stock price forecasting that integrates forest-based machine learning algorithms with the Golden Ratio strategy. Our proposed approach aims to leverage the predictive power of machine learning while incorporating insights from technical analysis to enhance trading performance and profitability. The methodology involves data collection and preprocessing, feature engineering, model development, and performance evaluation. Historical stock price data, financial indicators, and sentiment analysis data are gathered and pre-processed to ensure data quality. Feature engineering techniques are applied to extract relevant features, and forest-based machine learning algorithms are trained on the data to forecast stock prices. The Golden Ratio strategy is integrated into the trading model to identify the entry point and exit point. Empirical validation and performance evaluation demonstrate the effectiveness and robustness of the proposed approach. Through back testing and simulation-based testing, we evaluate the model's accuracy, profitability, and risk-adjusted returns. Our findings contribute to the advancement of algorithmic trading research and practice, offering insights into the potential benefits of combining machine learning algorithms with established trading strategies. The proposed hybrid model incorporates Golden Ratio Strategy, integrating Random Forest (RF). The results of the proposed random forest with Golden Ratio Strategy are compared with the results of individual model. This comparison helps in proving the efficiency of the proposed model. © 2024 IEEE.

关键词： Adversarial machine learning

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Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

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SN computer science 2024年第5期5卷 549页

作者： Hamza, Manar Ahmed Hashim, Aisha Hassan Abdalla Motwakel, Abdelwahed Elhameed, Elmouez Samir Abd Osman, Mohammed Kumar, Arun Singla, Chinu Munjal, Muskaan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur 53100 Malaysia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Management Information Systems College of Business Administration in Hawtat bani Tamim Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science College of Post-Graduated Studies University of Science and Technology Omdurman Sudan Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Natural Language Processing (NLP) with Deep Learning (DL) for Tweets Classification includes use of advanced neural network designs to analyse and classify Twitter messages. DL techniques like recurrent neural network (RNN) or transformer-based frameworks like BERT are used to mechanically learn difficult linguistic patterns and contextual info from tweet data. These techniques able to capture subtleties of language with sarcasm, sentiment, and context-specific meanings and making them suitable for tasks like sentiment analysis or topic classification in realm of social media. Leveraging deep symbols learned from great amounts of textual data, these NLP techniques permit precise and nuanced classification of tweets, donating to enhanced information retrieval, sentiment tracking, and trend analysis in dynamic and fast-paced world of social media communication. In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. The goal of the AOADL-TC technique is to identify and discriminate different kinds of sentiments that exist in the tweet data. At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. At last, tuning of parameters related to parallel BiGRU model performed by AOA. An wide set of tests carried out to illustrate better performance of AOADL-TC model. The experimental outcomes portrayed that AOADL-TC technique demonstrates the supremacy of the AOADL-TC technique in terms of different evaluation metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Machine learning Natural language processing Sentiment analysis Social media

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Detection of Fetal Cardiac Chamber Three Vessel Trachea View using Deep Learning 9

Detection of Fetal Cardiac Chamber Three Vessel Trachea View...

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9th International Conference on Electrical engineering, computer science and Informatics, EECSI 2022

作者： Sapitri, Ade Iriani Nurmaini, Siti Rini, Dian Palupi Rachmatullah, Muhammad Naufal Darmawahyuni, Annisa Gusendi, Arjuno Universitas Sriwijaya Doctoral in Engineering Sciences Faculty of Engineering Indonesia Universitas Sriwijaya Intelligent System Research Group Faculty of Computer Science Indonesia Universitas Sriwijaya Faculty of Computer Science Indonesia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

In fetal cardiology, echocardiographic imaging has been shown to aid in the diagnosis of fetal monitoring for the development of fetal heart function. The small heart size and the lack of expertise in fetal echocardiography of some sonography pose challenges in terms of research. The use of technology to enhance the image assists in the diagnosis of abnormalities important for optimal fetal cardiac assessment. Multi object detection is the most challenging but very informative task in basic machine learning. We must detect cardiac substructure precisely the condition of the fetal heart, especially in normal conditions. Knowing the normal heart substructure can provide information about the presence or absence of fetal heart abnormalities. Multiple ways to view fetal heart chambers, four chambers (4CH) basic standard cardiac scan, left and right ventricular outflow tract (LVOT/RVOT) focusing on structural pathways, and Three Tracheal Vessels (3VV/3VT) complex fetal heart scan confirms flow in the three blood vessels helps determine whether aortic arch problems are causing normal heart conditions or not. The ductus arteriosus (DUCT), ascending aorta (AoA), superior vena cava (SVC), and trachea. In particular, the framework performs detection using a deep learning detection model applied to 3VT ultrasound images. This study identifies the evaluation using the mean average precision (mAP). The results of the study detecting the normal condition of the fetal heart substructure gave good results on the results of the mAP evaluation of 87.55% and each object of DUCT, AoA, SVC, Trachea with AP values of 92%, 91%, 91%, 76%. © 2022 Institute of Advanced engineering and science (IAES).

关键词： Deep Learning Detection Multi Object Ultrasound Image

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Pre-trained Self-Attention Framework: An Efficient Mechanism for Source Separation 13th

Pre-trained Self-Attention Framework: An Efficient Mechanism...

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13th International Conference on Information Technology and its Applications, CITA 2024

作者： Minh Tan, Ha Fhadli, Muhammad Kim Quoc, Nguyen Vu, Duc-Quang Faculty of Information Technology Nguyen Tat Thanh University Ho Chi Minh City Viet Nam Department of Informatics Faculty of Engineering Khairun University Ternate City Indonesia Department of Computer Science and Information System Thai Nguyen University of Education Thai Nguyen Viet Nam

ISBN: (纸本)9783031741265

In this work, the pre-trained model of the self-attention framework is proposed for single-channel speech separation. Firstly, all layers in the pre-trained self-attention framework are frozen. The model is then retrained through three stages using the scheduling mechanism for learning rates and the layers of the framework are unlocked following the schedule. This way, the model is relearned, enhanced, and updated from previous knowledge. This is an effective way to improve the advanced model performance while significantly reducing the time and cost of a training model. This method is beneficial in applying existing models to perform a similar task or enhancing model performance. In this strategy, the pre-trained system outperforms the non-pre-trained system since the following phases of the model’s training repurpose characteristics extracted through the previously trained early phases. The suggested method is tested and assessed on a conventional dataset. The findings from experiments suggest that the methodology has higher performance than the baseline framework and outperforms current methods for the monaural speech separation task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： fine-tuning framework pre-trained model self-attention mechanism speech separation time masking framework Transformer

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Intelligent Deep Convolutional Neural Network Based Object DetectionModel for Visually Challenged People

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computer systems science & engineering 2023年第9期46卷 3191-3207页

作者： S.Kiruthika Devi Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Mohamed K.Nour Ahmed Ashour Anwer Mustafa Hilal Department of Computing Technology SRM Institute of Science and TechnologyIndia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Engineering Mathematics and Physics Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial Intelligence(AI)and computer Vision(CV)advancements have led to many useful methodologies in recent years,particularly to help visually-challenged *** detection includes a variety of challenges,for example,handlingmultiple class images,images that get augmented when captured by a camera and so *** test images include all these variants as *** detection models alert them about their surroundings when they want to walk *** study compares four CNN-based pre-trainedmodels:ResidualNetwork(ResNet-50),Inception v3,DenseConvolutional Network(DenseNet-121),and SqueezeNet,predominantly used in image recognition *** on the analysis performed on these test images,the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and *** further improve the performance of the Inception v3 model,the thermal exchange optimization(TEO)algorithm is applied to tune the hyperparameters(number of epochs,batch size,and learning rate)showing the novelty of the *** accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer,hyperparameter optimizer,and factorization ***,Inception V3 can handle images of different *** makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things(IoT)-based devices.

关键词： Pre-trained models object detection visually challenged people deep learning Inception V3 DenseNet-121

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Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

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computer systems science & engineering 2023年第9期46卷 3103-3119页

作者： Khaled M.Alalayah Fatma S.Alrayes Mohamed K.Nour Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsNajran UniversitySharurahSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

Malware is a‘malicious software program that performs multiple cyberattacks on the Internet,involving fraud,scams,nation-state cyberwar,and *** malicious software programs come under different classifications,namely Trojans,viruses,spyware,worms,ransomware,Rootkit,botnet malware,*** is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it;then,the user pays a ransom procedure of a sum of *** prevent detection,various forms of ransomware utilize more than one mechanism in their attack flow in conjunction with Machine Learning(ML)*** study focuses on designing a Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection(LBAAA-OMLMD)approach in computer *** presented LBAAA-OMLMDmodelmainly aims to detect and classify the existence of ransomware and goodware in the *** accomplish this,the LBAAA-OMLMD model initially derives a Learning-Based Artificial Algae Algorithm based Feature Selection(LBAAA-FS)model to reduce the curse of dimensionality ***,the Flower Pollination Algorithm(FPA)with Echo State Network(ESN)Classification model is *** FPA model helps to appropriately adjust the parameters related to the ESN model to accomplish enhanced classifier *** experimental validation of the LBAAA-OMLMD model is tested using a benchmark dataset,and the outcomes are inspected in distinct *** comprehensive comparative examination demonstrated the betterment of the LBAAAOMLMD model over recent algorithms.

关键词： computer networks machine learning security malware detection feature selection ransomware

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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