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Multi Parameter Prediction Using Vector Autoregressive and Autoregressive Integrated Moving Average with Exogenous: A Case Study of Greenhouse of Melon Cultivation in Blitar Regency 2

Multi Parameter Prediction Using Vector Autoregressive and A...

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2nd International Conference on computer System, Information Technology, and electrical engineering, COSITE 2023

作者： Susetyoko, Ronny Hermawan, Hendhi Satriyanto, Edi Purwantini, Elly Iman, Budi Nur Puspita, Eru Darojah, Zaqiatud Fadliana, Alfi Wijayanto, Ardik Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Department of Electrical Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Department of Mechanics and Energy Politeknik Elektronika Ne geri Surabaya Surabaya Indonesia

ISBN: (纸本)9798350343069

Melon cultivation is one of the contributors to economic growth in the Food Crops and Horticulture sub-sector in East Java. Currently, in Wates District, Blitar Regency, there are more than 300 greenhouse units with an average area of 700 m2 for the cultivation of sakata and inthanon melons. Real-time monitoring is needed by farmers to know for certain the quality of greenhouse conditions, especially the complicated conditions of melon cultivation. This study aims to predict real-time multi parameter greenhouse conditions using Vector Autoregressive (VAR). VAR performance compared to the Autoregressive Integrated Moving Average with Exogenous (ARIMAX) model based on MAE and MAPE. The results of this study, the VARI(3,1) model with a data train length of 400 has MAE|MAPE humidity, light intensity, and temperature respectively 0.618|0.655%, 2.135|2.735%, and 0.079|0.223%. VARI(3,1) as a simultaneous model is effectively applied to predict humidity and temperature with an average MAE|MAPE of 0.226|0.277% and 0.029|0.109% respectively with a test data length of 100-6000. The results of light intensity prediction with the VARI(3,1) model have MAE| MAPE is quite large (1.634|11.124%). © 2023 IEEE.

关键词： Greenhouses

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Optimizing Power Allocation and Beamforming in mmWave-NOMA Downlink with Deep Reinforcement Learning

Optimizing Power Allocation and Beamforming in mmWave-NOMA D...

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Communications and Networking (MECOM), IEEE Middle East Conference on

作者： Abbas Akbarpour Kasgari Chrysostomos Chrysostomou Department of Electrical Engineering Computer Engineering and Informatics Frederick University and Frederick Research Center Nicosia Cyprus

ISBN: (数字)9798350376715

ISBN: (纸本)9798350376722

The high demand for data rates in the fifth generation (5G) and beyond of wireless communication can be met by the Non-Orthogonal Multiple Access (NOMA) approach in the millimeter-wave (mmWave) frequency band. Joint power allocation and beamforming for 5 G and beyond mmWave-NOMA systems are essential, which can be achieved through optimization approaches. To this end, we have employed a Deep Reinforcement Learning (DRL) approach for policy generation, leading to an optimized sum-rate for users. Unlike existing methods, our approach is not susceptible to channel impulse response (CIR) estimation errors. The actor-critic framework is utilized to measure the immediate reward and provide new actions to maximize the overall Q-value of the network. The immediate reward is defined based on the summation of the rates of two users, considering the minimum guaranteed rate for each user and the total consumed power as constraints. The simulation results demonstrate the superiority of the proposed approach compared to state-of-the-art counterparts, including Time-Division Multiple Access (TDMA) and NLOS-NOMA optimization strategies, in terms of sum-rate of users, while considering both perfect and imperfect channel state information (CSI) at the receiver. This work can also be beneficial for communications in vehicular networks, particularly in applications involving Collective Perception Messages (CPMs) dissemination for autonomous driving.

关键词： Wireless communication NOMA Time division multiple access Array signal processing 5G mobile communication Deep reinforcement learning Resource management Millimeter wave communication Channel impulse response Optimization

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Preliminary Study on the Reproducibility of Fix Templates in Static Analysis Tool 35

Preliminary Study on the Reproducibility of Fix Templates in...

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35th IEEE International Conference on Software engineering Education and Training, CSEE and T 2023

作者： Kim, Sohyun Kim, Youngkyoung Lee, Eunseok Sungkyunkwan University Department of Software Suwon Korea Republic of Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Sungkyunkwan University College of Computing and Informatics Suwon Korea Republic of

ISBN: (纸本)9798350322026

Automated Program Repair (APR) automatically generates patches for identified defects. As a result, APR can encourage novice students to learn coding by providing appropriate patches. Since students have their coding conventions, we should be able to deal with many types of defects. Existing studies have used predefined RuleId-driven templates to automatically fix defects in Static Analysis Tools(SATs). However, the community periodically adds, deletes, or changes SAT's RuleIds. This is difficult for existing RuleId-based templates to reflect those changes immediately. Existing studies only cover about 10 RuleIds, making it difficult to address all defects faced by all students. Therefore, it is necessary to establish appropriate criteria for classifying templates. The SAT has a predefined format for how Error Messages are written, and since Error Messages contain fixing actions, defects with similar Error Messages tend to have similar fixing actions. These characteristics of the Error Message are suitable for reproducible template classification criteria. Our preliminary study demonstrated that by classifying patterns based on Error Messages, we could effectively address various defects, including those in different programming languages, using a single template. This means that if a newly added RuleId corresponds to an Error Message format already in the predefined Error Message-based template, it can be modified without additional effort. We plan to construct reproducible templates for each Error Message and provide ongoing patching of defects to students. © 2023 IEEE.

关键词： Defects

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Characteristics of Accuracy Function on Multiclass Classification Based on Best, Average, and Worst (BAW) Subset of Random Forest Model 24

Characteristics of Accuracy Function on Multiclass Classific...

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24th International Electronics Symposium, IES 2022

作者： Susetyoko, Ronny Yuwono, Wiratmoko Purwantini, Elly Iman, Budi Nur Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Department of Electrical Engineering Surabaya Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

This study aims to determine the effect of percentage of training data and number of features on average accuracy of classification model. Characteristics of the accuracy function of 2 datasets with different feature data types were also studied. All possible random forest models were calculated for accuracy based on some percentage level of training data and number of features. The accuracy function was constructed based on subsets of best accuracy (Best), average accuracy (Average), and worst accuracy (Worst) using polynomial regression. There were no significant difference in the average accuracy of various percentage levels of training data. However, there were significant difference in the average accuracy based on the number of features. The best polynomial model for average accuracy function of the 2 datasets had R2 99.50% and R2 96.99%, respectively. Based on individual testing (with a 95% confidence level), there was a linear effect of the percentage of training data on the average accuracy, and there were linear and quadratic effects on the number of features on the average accuracy. In the first dataset, classification model using 7 features obtain classification accuracies ranging from 96.9%-97.6%. In the second dataset using 7 features, the classification accuracies range from 74.0%-75.0%. Characteristics of the Best, Average, and Worst accuracy (BAW) functions could be an important description of the accuracy function characteristics and to determine the number of features. © 2022 IEEE.

关键词： Decision trees

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IoT for Sustainable Hospitality: A Systematic Review of Opportunities and Challenges for the Hospitality Industry Revolution 19

IoT for Sustainable Hospitality: A Systematic Review of Oppo...

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19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Poullas, Marios S. Kakoulli, Elena Neapolis University Pafos Intelligent Systems Laboratory Department of Computer Science Pafos Cyprus Cell and Developmental Biology University College London London United Kingdom Cyprus University of Technology Electrical Engineering and Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798350346497

Technology has influenced the hospitality industry, and the Internet of Things (IoT) has become a focus for sustainability, revenue growth, and problem-solving. This systematic review examined the opportunities and challenges of implementing IoT technology for a sustainable hospitality. Through a thorough analysis of 28 studies, we found that IoT technology has great potential to address sustainability issues in the hospitality industry. However, there are still significant challenges that need to be addressed for the successful implementation of IoT technology in the hospitality industry. Overall, this study highlights the potential of IoT technology to revolutionize the hospitality industry and make it more sustainable. Further research is needed to address the challenges of IoT implementation in the hospitality industry and to evaluate the effectiveness of IoT technology in achieving sustainability goals. © 2023 IEEE.

关键词： Energy efficiency

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The Implementation of Hybrid Semantic Ontology-based Model on Movie Recommendation System

The Implementation of Hybrid Semantic Ontology-based Model o...

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2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Ifada, Noor Cahyaningrum, Evinda Widia Rachman, Fika Hastarita Department of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

ISBN: (纸本)9781665476508

This paper adopts the Hybrid Semantic Ontology-based (HSO) model for a movie recommendation system. HSO consists of Collaborative Filtering (CF) and Content-based (CB) modules that respectively implement Matrix Factorization (MF) and ONTO Semantic Similarity algorithms. Since the feedback data type influences the MF algorithm choice, we individually implement the Non-Negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD) algorithms for handling the movie rating data. Accordingly, our proposed methods are called HSO-NMF and HSO-SVD. Meanwhile, since the domain determines the ontology, we build and use a new movie ontology on the CB module. The experiments show that HSO performs the best when implemented using the SVD algorithm. On average, the increased percentages of HSO-SVD to HSO-NMF are 1.18% and 1.62% in Precision and NDCG metrics, respectively. The experiments also show that implementing the Hybrid model yields more accurate results than the CB or CF model. © 2022 IEEE.

关键词： Collaborative filtering

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Implementation of MTCNN Facial Feature Extraction on Sleepiness Scale Classification Using CNN

Implementation of MTCNN Facial Feature Extraction on Sleepin...

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2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Putra, Adima Mahardika Zaini, Ahmad Pramunanto, Eko Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9781665476508

Sleepiness is a condition when the level of human consciousness decreases. Sleepiness is not easy to measure externally. If this is allowed just like that, it would be very dangerous if we were doing activities that requires full control of consciousness such as activities driving. This set of tools and methods for detecting drowsiness has been developed. However, in its implementation the intrusive method uses this tool less practical. In addition, the sleep detection method uses video images as well experiencing problems due to the potential for data loss in the form of facial features. To get this data, you must use a near infrared camera which has a low resolution and lacks detail. Therefore it is necessary an algorithm that is able to detect facial features to the maximum for classifying a person's sleepiness scale. To achieve the goal, a program will be created that functions to perform the extraction facial features and eye features. The program will function to detect number of frames containing facial features and performing condition classification eyes closed or open which will then be saved in a 'csv' file to be processed. Furthermore, the data will be carried out in the training process using 1D CNN architecture. The results of the training process that has been carried out The previous model is the model that will be used in performing the scale classification drowsiness. There are 6 experimental scenarios to get the best results. From all the results that have been obtained, it can be concluded that the best model results are the result of the training process with epoch values by 30 and the addition of synthetic data. Accuracy value obtained of 89% and the loss value of 34%. © 2022 IEEE.

关键词： Classification (of information)

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Five-Stage Pipelined 32-Bit RISC-V Base Integer Instruction Set Architecture Soft Microprocessor Core in VHDL 23

Five-Stage Pipelined 32-Bit RISC-V Base Integer Instruction ...

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23rd International Seminar on Intelligent Technology and Its Applications, ISITIA 2022

作者： Phangestu, Aaron Elson Mujiono, Ir. Totok Kom, M.I. Ahmad Zaini, St Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Computer Engineering Department Surabaya60111 Indonesia

ISBN: (数字)9781665460811

ISBN: (纸本)9781665460811

Proprietary technologies with complicated licensing currently dominate the microprocessor industry. As a result, we must seek out a freely available, open-source alternative. In this paper, we discussed the implementation of a five-stage pipelined soft processor core. The core uses the RISC-V RV32I Base Integer Instruction Set Architecture. RISC-V is an open standard ISA that is freely available to use and modify. To implement our processor core, we followed the FPGA design methodology. First, we implemented the design specification with the VHDL Hardware Description Language. Then, siwe mulated the design in the ModelSim simulation environment. Following the verification, we analyzed the resource usage, critical path, and maximum frequency of the processor, then uploaded the processor core to an actual Cyclone IV EP4CE6E22C FPGA. Our CPU core successfully executed all the RV32I instructions, except FENCE, ECALL, and CSR instructions. The proposed processor core runs on 2115 LUTs, 558 flip-flops, and 67,608 memory bits, with a maximum frequency of 62.95 MHz. © 2022 IEEE.

关键词： computer hardware description languages

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AI and ML Applications Traffic: Designing Challenges for Performance Optimization of Optical Data Center Networks

AI and ML Applications Traffic: Designing Challenges for Per...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Peristera Baziana Georgios Drainakis David Georgantas Adonis Bogris Department of Informatics and Telecommunications University of Thessaly Greece School of Electrical and Computer Engineering National Technical University of Athens Greece Department of Informatics and Computer Engineering University of West Attica Greece

ISBN: (数字)9789532901382

ISBN: (纸本)9798350354614

Artificial intelligence (AI) and machine learning (ML) are vastly becoming key parts of today’s applications, supporting diverse human activities and decision making. New training models are used requiring an explosive traffic exchange among data centers (DCs) servers, putting tremendous pressure on DC network (DCN). Therefore, new DCN design challenges are required to efficiently handle the emerging AI/ML applications traffic. Contrary to existing works that do not account for these challenges, in this paper we introduce an optical intra-rack DCN architecture, as an optical local area network (LAN) paradigm, and an efficient wavelength division multiplexing access (WDMA) algorithm tailored to serve the AI/ML traffic. For the proposed WDMA scheme, we assume diverse AI/ML traffic classes according to the required service time and quality of service (QoS) delivery and we adopt appropriate access rules for each of them to achieve high DCN performance and access fairness. Simulation results show that our proposal reaches high bandwidth utilization ($90 \%$ under nominal load 400 Gbps) and low end-to-end latency (<300 ns across all loads for high/medium QoS traffic, and <100 $\mu \mathrm{s}$ under nominal load for low QoS traffic), contributing to the agenda of new design challenges for optical DCNs suitable for AI/ML traffic.

关键词： Training Data centers Protocols Optical design Quality of service computer architecture Traffic control Wavelength division multiplexing Servers Local area networks

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Optimizing Art Studio Connectivity: A Haversine and Greedy Algorithm Approach for Navigation in Cirebon Indonesia 17

Optimizing Art Studio Connectivity: A Haversine and Greedy A...

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17th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2023

作者： Permana, Indra Surya Arlovin, Teguh Hidayat, Taufik Sarief, Ivany Solihin, Hanhan Hanafiah Mulyadi, Cecep Deni Institut Teknologi Dan Kesehatan Mahardika Department of Informatics Engineering Cirebon Indonesia Universitas AMIKOM Department of Computer Engineering Yogyakarta Indonesia Universitas Indonesia Department of Electrical Engineering Jakarta Indonesia Universitas Sangga Buana Faculty of Engineering Department of Electrical Engineering Bandung Indonesia Universitas Sangga Buana Department of Information System Bandung Indonesia Universitas Sangga Buana Faculty of Engineering Bandung Indonesia

ISBN: (纸本)9798350309164

The proposed geographic information system (GIS) provides a systematic approach to locating and connecting art studios, enabling cultural institutions, artists, and students to collaborate and share resources more efficiently. The implementation of a structured GIS and Haversine Formula and Greedy Algorithm can improve the management and connectivity of art studios in Cirebon, fostering connections and collaboration between art studios, artists, and cultural institutions. This research used a greedy algorithm to find the shortest route from user's current location to the destination point, i.e., Art Gallery. The Greedy Algorithm is a problem-solving approach that aims to find an optimal solution by iteratively selecting the best local option. The Haversine formula is used to calculate the distance from each connected node using the haversine formula, which converts latitudes and longitudes from degrees to radians and calculates the differences in latitude and longitude. In implementing the nearest art studio search system, the Haversine Formula combined with Greedy Algorithm can be employed as an alternative for search logic since it can directly calculate the distance from the initial point or user's selected point by utilizing easily identifiable reference points. © 2023 IEEE.

关键词： Studios

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