1 10 24 https://repository.horizon.ac.id/files/original/24a4515e9c0ddbc7d27dc8158defbf74.pdf 44900f1d1cb5771656bedfbe9cc1ba60 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.3 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A circular compact ultra‐wideband antenna for 5G microwave applications Subject The topic of the resource 5G Current distribution Gain Slots Ultra‐wideband Description An account of the resource This article introduces an innovative circular and compact ultra‐wideband (UWB) radiator designed specifically for 5G microwave applications. This antenna incorporates a “TU”-shaped ground plane on its reverse side, with strip lines feeding the circular element on the front side. Notably, the antenna exhibits impressive characteristics, including an outstanding impedance bandwidth of 107%, and an impressive return loss of -32 dB. Its operational frequency range spans from 2.4 GHz to 11 GHz, centered at 6.7 GHz. Extensive simulations were conducted using CST microwave studio software to validate its performance. The antenna’s physical dimensions are defined by a size of 0.12 λ × 0.08 λ × 0.012 λ relative to its wavelength. Furthermore, this antenna demonstrates exceptional stability in its polar patterns and maintains a high-efficiency level, achieving a substantial gain of 3.75 dBi with an efficiency rating of 84.5%. These remarkable attributes make this antenna suitable for a wide range of applications, including Wi-Fi, 5G, WLAN, and various other microwave communication scenarios. Creator An entity primarily responsible for making the resource Swati Varun Yadav1, Manish Varun Yadav2, Tanweer Ali3, Sounik Kiran Kumar Dash4, Navya Thirumaleshwar Hegde2, Vishnu G. Nair2 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Mar 10, 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT 5G Current distribution Gain Slots Ultra‐wideband https://repository.horizon.ac.id/files/original/db539eb90d3e8ebff0686b087ef21d4f.pdf ce64b919a70f5299be8ccfd16ad7063b Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.3 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A comprehensive evaluation of multiclass imbalance techniques with ensemble models in IoT environments Subject The topic of the resource Imbalanced ratio Internet of things Intrusion detection system Machine learning Multiclass Oversampling Weighted extreme gradient boosting Description An account of the resource The internet of things (IoT) has revolutionized connectivity and introduced significant security challenges. In this context, intrusion detection systems (IDS) play a crucial role in detecting attacks in IoT environments. Bot-IoT datasets often face class imbalance issues, with the attack class having significantly more samples than the normal class. Addressing this imbalance is essential to enhance IDS performance. The study evaluates various techniques, including imbalance ratio techniques we call imbalance ratio formula (IRF) for controlling imbalance data, while also testing IRF to compare it with oversampling techniques like synthetic minority oversampling technique (SMOTE) and adaptive synthetic sampling (ADASYN). This research also incorporates the extreme gradient boosting (XGBoost) ensemble model approach to improve IDS performance in dealing with multiclass imbalance issues in Bot-IoT datasets. Through in-depth analysis, we identify the strengths and weaknesses of each method. This study aims to guide researchers and practitioners working on IDS in high-risk IoT environments. The proposed IRF, when integrated with the XGBoost algorithm has been demonstrated to achieve comparable accuracy of 99.9993% while reducing the training time to be on average at least two times faster than those achieved by the other state-of-the-art ensemble methods. Creator An entity primarily responsible for making the resource Januar Al Amien1, Hadhrami Ab Ghani2, Nurul Izrin Md Saleh3, Soni1, Yulia Fatma1, Regiolina Hayami1 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 7, 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Imbalanced ratio Internet of things Intrusion detection system Machine learning Multiclass Oversampling Weighted extreme gradient boosting https://repository.horizon.ac.id/files/original/94dc73bd239e1045b7042466083ba4b0.pdf c7f10046b60f3cd27b7a0f710b70d68e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.3 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A good result of brain tumor classification based on simple convolutional neural network architecture Subject The topic of the resource Brain tumor Convolutional neural network High-grade glioma Image classification Low-grade glioma Description An account of the resource Brain tumor disease has become a topic of research whether it is in the case of segmentation or classification. For the case of classification, the types of brain tumors that are grouped generally consist of high-grade glioma (HGG) and low-grade glioma (LGG) tumors. In this research we are doing, we propose a method for classifying 2 types of tumors, namely HGG and LGG, using the convolutional neural network (CNN) algorithm which is trained and will be tested against the 2018 and 2019 brain tumor segmentation (BRATS) datasets which have 4 modalities, namely fluid-attenuated inversion recovery (FLAIR), T1, T1ce, and T2 totaling 2048 images. The CNN algorithm was chosen because it can directly receive input in the form of a magnetic resonance image (MRI) with the feature extraction process as well as the classification algorithm. By forming a simple CNN algorithm architecture with only 3 convolutional layers which have an input layer in the form of a full MRI image with dimensions of 240×240×3, we obtained a relatively high accuracy result of 94.14%, it can even be said to be better than similar methods but with more complicated architecture. Creator An entity primarily responsible for making the resource Eko Hari Rachmawanto1, Christy Atika Sari1, Folasade Olubusola Isinkaye2 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Jan 19, 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Brain tumor Convolutional neural network High-grade glioma Image classification Low-grade glioma https://repository.horizon.ac.id/files/original/959fdebe84d202cff376c9747c77a7af.pdf e77442765d814b74f3a506dbd9074f3c Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.3 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A neuro-game model for analyzing strategies in the dynamic interaction of participants of phishing attacks Subject The topic of the resource Artificial neural network Cryptocurrency Game model Information security Phishing Description An account of the resource A dynamic model of countering phishing attacks is considered. Cryptocurrency exchanges (CCE) and/or their clients are considered as an example of a phishing victim. The model, unlike similar ones, is based on the assumption that the dynamics of the states of the player-victim of phishing attacks and the player-intruder (fisher) is set by means of a system of differential equations. The peculiarity of this model is that it represents a bilinear differential game of quality, for which methods for solving linear differential games are not applicable and, in addition, the absence of functional restrictions on the strategies of players (even immeasurable functions are allowed) does not allow the use of traditional approach. And their solution makes it possible to form payoff matrices, which are part of the training set for artificial neural networks (ANNs). Such a collaboration of models will make it possible to accurately build an anti-phishing strategy, minimizing the costs of both a potential victim of phishing attacks and the defense side when building a secure system of communication with CCE clients. The neuro-game approach makes it possible to predict the process of countering phishing in the context of costs for both parties using different strategies. Creator An entity primarily responsible for making the resource Valery Lakhno1,5, Volodimir Malyukov1, Inna Malyukova2, Bakhytzhan Akhmetov3,5, Zhuldyz Alimseitova4,5, Atkeldi Ogan4,5 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 27, 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Artificial neural network Cryptocurrency Game model Information security Phishing https://repository.horizon.ac.id/files/original/525333ffaacd198d10f9c6e518e83e81.pdf 006064e8dba08581ac08dc444fdae05f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.3 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A stacking ensemble model with SMOTE for improved imbalanced classification on credit data Subject The topic of the resource Banking data Classification Credit risk Stacking ensemble Synthetic minority over-sampling technique Description An account of the resource This research is based on a significant problem in credit risk analysis in the banking sector caused by class imbalance. We face the problem of the model’s inability to accurately identify risks in the ‘‘Charged Off’’ class. As a solution, we propose a stacked ensemble approach that utilizes synthetic minority over-sampling technique (SMOTE) to balance the class distribution. Experiments were conducted by applying SMOTE to the training data before training the credit model using gradient boosting (XGBoost) and random forest (RF) algorithms in a single ensemble. The results show significant improvements in precision, recall, and F1-score after applying SMOTE on the unbalanced classes. The updated model achieved a striking accuracy rate of 0,97 on resampled training data. This re-search clearly identifies the problem of class imbalance as a major challenge in credit risk analysis. The application of SMOTE in a stacked ensemble was found to be effective in improving model performance, making a valuable contribution to the development of more reliable credit models for better risk management and revenue generation in financial institutions. Creator An entity primarily responsible for making the resource Nur Alamsyah1, Budiman2, Titan Parama Yoga1, R. Yadi Rakhman Alamsyah2 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 27, 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Banking data Classification Credit risk Stacking ensemble Synthetic minority over-sampling technique https://repository.horizon.ac.id/files/original/9ca25b0e4ec739efb1581144a6453013.pdf 4dd83338be53fff73eb91b207345cbf3 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.3 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A survey on the dataset, techniques, and evaluation metric used for abstractive text summarization Subject The topic of the resource Abstractive summarization Attention mechanism Automatic text summarization Deep learning Extractive summarization Transformers Description An account of the resource Whenever there is too much information out there, it is desirable to summarize. If humans are trying to create the summary, it will take lot of time. Now to make the problem of summarizing information easier and more effortless one can automate the summarization process which can reduce the time taken in creating summary. This is called as automatic summarization. The two ways of summarization are extractive summarization and abstractive summarization. Extractive summarization and its applications have been the subject of extensive research and have received state of art solution. But abstractive summarization still is a progressive field as it is difficult to create abstractive summary as humans do. Also, it is still a question i.e., how to evaluate the quality of a summary? Therefore, this paper is a comprehensive survey on the dataset used with its details and statistics, analysis of various abstractive summarization techniques and important parameters for evaluating the quality of summary. Deep leaning based models have given new direction in this field. The author also focuses on problems and challenges faced in the generation of summary which are opening the future research scope in this domain. Creator An entity primarily responsible for making the resource Shivani Sharma1, Gaurav Aggarwal1, Bipin Kumar Rai2 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 8, 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Abstractive summarization Attention mechanism Automatic text summarization Deep learning Extractive summarization Transformers https://repository.horizon.ac.id/files/original/c71d6a74cce9c6d882879bf618bbe3c8.pdf 115d01d3daf68d15534467089f62df56 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.3 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Adaptive handover control parameters based on cell load capacity in a B5G/6G heterogeneous network Subject The topic of the resource Adaptive handover parameters B5G/6G Environmental constraints Handover decision Throughput Description An account of the resource In the B5G/6G network, the deployment of small cells increased to keep up with the growth of mobile traffic. This deployment will increase the number of handovers (HOs) between cells. Ping-pong handover (PPHO) and radio link failure (RLF) are considered the two major problems in HO that may occur. So, the challenge is to set the handover control parameters (HCPs) carefully to find out the proper HO decision that should be appropriate to the environmental constraints. Therefore, in our paper, we propose an adaptive HCPs algorithm that adapts to environmental constraints. In addition, the proposed algorithm will have immunity to RLF and will significantly minimize the amount of PPHO compared to other workers. In the simulation results, our proposed model is evaluated using two frequency plans. By using frequency plan 1, the user mean throughput increased from 270 Kbps to 281 Kbps when the serving cell was fully loaded. By using frequency plan 2, the user mean throughput increased from 6 Mbps to 20 Mbps when the serving cell was fully loaded. In addition, the amount of ping-pong handover between overlapped small cells decreased and will not exceed one PPHO compared to another literature model. Creator An entity primarily responsible for making the resource Maryhan M. Mohamed1, Hesham M. Elbadawy2, Abdelhady Abdelazim Ammar Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 3, 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/6d30e3112e1837e94aea522ecee386c6.pdf 2af8fb9b6c2d3afde7bf342dff8745b5 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.3 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Adoption of tax digitalisation among Malaysian tax practitioners Subject The topic of the resource Adoption Digitalisation Practitioners Tax Unified theory of acceptance and use of technology Description An account of the resource The rapid advancement of digitalization has significantly impacted various aspects of accounting professions, particularly in taxation. Tax digitization offers numerous advantages, including streamlining tax processes, reducing administrative burdens, increasing efficiency, and enhancing data security. While tax practitioners in advanced economies have embraced digitalization, their Malaysian counterparts are still in the early stages of transitioning to a modern digital system. This situation has prompted researchers to predict factors that could accelerate the adoption of tax digitalization among Malaysian tax practitioners. Emulating the Unified theory of acceptance and use of technology (UTAUT), this study investigates the adoption of tax digitalization with performance expectancy, effort expectancy, social influence, and facilitating conditions. The researchers distributed 200 questionnaires to Malaysian tax practitioners. However, only 142 proceeded for further analysis. Results from multiple regression using partial least squares structural equation modelling (PLS-SEM) 3 indicate that all variables: effort and performance expectancy, social influence, and facilitating conditions exhibit a significant relationship with tax digitalization adoption. These findings provide valuable insights for policymakers, tax authorities, and professional bodies in developing strategies and initiatives to promote the adoption of tax digitalization among practitioners. Embracing digitalization is crucial for transforming the profession and fostering efficiency, sustainability, and resilience. Creator An entity primarily responsible for making the resource Maheran Zakaria1, Wan Noraswaniaty Wan Ahmad1, Nuraini Che Hussin2, Rabaatul Azira Hassan1, Marziana Madah Marzuki1, Muhammad Syukur3, Eka Nurmala Sari4 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 8, 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Adoption Digitalisation Practitioners Tax Unified theory of acceptance and use of technology https://repository.horizon.ac.id/files/original/3a7aafeb52a50030844121b40a3c82f9.pdf 99d4954fc225abc9a2f7ecd9f8e71b81 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.3 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Advancements in accurate speech emotion recognition through the integration of CNN-AM model Subject The topic of the resource Attention mechanism Convolution neural network Emotion Recognition Signal Speech Description An account of the resource In this study, we introduce an innovative approach that combines convolutional neural networks (CNN) with an attention mechanism (AM) to achieve precise emotion detection from speech data within the context of e-learning. Our primary objective is to leverage the strengths of deep learning through CNN and harness the focus-enhancing abilities of attention mechanisms. This fusion enables our model to pinpoint crucial features within the speech signal, significantly enhancing emotion classification performance. Our experimental results validate the efficacy of our approach, with the model achieving an impressive 90% accuracy rate in emotion recognition. In conclusion, our research introduces a cutting-edge method for emotion detection by synergizing CNN and an AM, with the potential to revolutionize various sectors. Creator An entity primarily responsible for making the resource Marion Olubunmi Adebiyi1, Timothy T Adeliyi2 , Deborah Olaniyan1, Julius Olaniyan1 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Date A point or period of time associated with an event in the lifecycle of the resource Feb 21, 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Attention mechanism Convolution neural network Emotion Recognition Signal Speech https://repository.horizon.ac.id/files/original/6b92a501b659261f901dbe6ff17f5f0e.pdf 594619dd4c53033f6a855ac6f55d7f86 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 22, NO.3 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Advancements in wind farm layout optimization: a comprehensive review of artificial intelligence approaches Subject The topic of the resource Artificial-intelligence Metaheuristic algorithms Optimal placement Renewable energy Review Wind farm Wind turbines Description An account of the resource This article provides a detailed evaluation of cutting-edge artificial intelligence (AI) approaches and metaheuristic algorithms for optimizing wind turbine location inside wind farms. The growing need for renewable energy sources has fueled an increase in research towards efficient and sustainable wind farm designs. To address this challenge, various AI techniques, including genetic algorithms (GA), particle swarm optimization (PSO), simulated annealing, artificial neural networks (ANNs), convolutional neural networks (CNNs), and reinforcement learning, have been explored in combination with metaheuristic algorithms. The goal is to discover optimal sites for turbine placement based on a variety of parameters such as energy output, cost-effectiveness, environmental impact, and geographical restrictions. The paper examines the advantages and disadvantages of each strategy and highlights current breakthroughs in the area. This assessment adds to continuing efforts to optimize wind farm design and promote the use of clean and sustainable energy sources by offering significant insights into current advances. Creator An entity primarily responsible for making the resource Mariam El Jaadi1, Touria Haidi1, Abdelaziz Belfqih2 Source A related resource from which the described resource is derived Journal homepage: http://telkomnika.uad.ac.id Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT Artificial-intelligence Metaheuristic algorithms Optimal placement Renewable energy Review Wind farm Wind turbines