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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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The Effect of Information Security Awareness, Subjective Norms and Shared Tacit Assumptions on Information Security Culture 2

The Effect of Information Security Awareness, Subjective Nor...

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2nd Zimbabwe Conference on Information Communication and Technology, ZCICT 2023

作者： Mupokosera, Joseph Mphahlele, Maredi Ivan Jordaan, Annelie Jokonya, Osden Tshwane University of Technology Department of Informatics Pretoria South Africa Tshwane University of Technology Department of Computer Engineering Pretoria South Africa Vaal University of Technology Department of Informatics Vanderbijlpark South Africa University of the Western Cape Department of Information Systems Cape Town South Africa

ISBN: (纸本)9798350318128

Financial services institutions are one of the prime targets of information security breaches because of the potential of the attackers to reap financial rewards from launching the attacks. Information security culture which is a collection of socio-cultural processes implemented by firms, is forecast to forestall the scourge of information security breaches. Information security culture targets to safeguard the information assets of firms through influencing employees to conduct themselves in a manner that is compatible with the protection of firms' assets. Information security awareness, subjective norms and shared tacit assumptions are proposed as some of the main determinants of information security culture in the financial services. Therefore, this paper investigates the impact that information security awareness, subjective norms and shared tacit assumptions exert on financial services firms' information security culture. This paper is a quantitative case study of four financial services firms in Zimbabwe. The results of this study show that information security awareness and subjective norms are predictors of information security culture in the financial services. Conversely, the study established that shared tacit assumptions are not related to information security culture in the financial services. © 2023 IEEE.

关键词： Finance

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Traffic Sign Recognition and Auditory Alert System for Sri Lankan Drivers Using Deep-Learning

Traffic Sign Recognition and Auditory Alert System for Sri L...

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2024 International Research Conference on Smart Computing and Systems engineering, SCSE 2024

作者： Vimalambikaipakan, Geerthana Amarasinghe, Chinthaka Rajapaksha, Tharangani Thayananthan, Thevathayarajh Mariyathas, Janotheepan Uva Wellassa University Department of Science and Technology Badulla Sri Lanka Mississippi State University Department of Agricultural and Biological Engineering Mississippi State United States Uva Wellassa University Department of Computer Science and Informatics Badulla Sri Lanka

ISBN: (纸本)9798350375688

Traffic signs are essential for road safety, guiding and informing drivers about rules and conditions. Ignoring signs or traffic rules can lead to accidents, causing property damage, and even deaths. To ensure safety, drivers must remain aware of traffic signs, obey their instructions, and drive responsibly. Notably, Sri Lanka has unique traffic signs compared to other countries. This study aims to develop a deep learning-based traffic sign alert system with good accuracy and efficiency in traffic sign recognition and enhance driver awareness by integrating an auditory alert system. This alert system was implemented under two main sections: object detection system and auditory alert system. The traffic signs dataset images were labeled and pre-processed using the Roboflow software tool. The You Only Look Once version 5 (YOLOv5) model was used to train the system, which allowed it to understand complicated patterns and attributes associated with 15 different sign classes. To improve driver awareness, an auditory alert system was developed using the PyDub library to generate real-time alerts in the form of voice messages. These alerts are triggered whenever a traffic sign is detected, giving an additional layer of information to the driver. On the test dataset, the integrated and fine-tuned YOLOv5 model achieved the F1 score of 90.09% and mean average precision (mAP) of 87.55% resulting in good evaluation metrics values for detection. This YOLOv5 model-based traffic sign auditory alert system was highly effective and efficient in increasing driver awareness and also helped to reduce road accidents. © 2024 IEEE.

关键词： Traffic signs

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Improved IP lookup technology for trie-based data structures

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Journal of computer and System Sciences 2023年 133卷 41-55页

作者： Lin, Yen-Heng Hsieh, Sun-Yuan Master Degree Program on Artificial Intelligence National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering Institute of Manufacturing Information Systems Institute of Medical Informatics National Cheng Kung University Tainan701 Taiwan Department of Computer Science and Information Engineering National Chi Nan University Nantou Puli Township54561 Taiwan

Many Internet protocol (IP) lookup algorithms have been formulated to improve network performance. This study reviewed and experimentally evaluated technologies for trie-based methods that reduce memory access, memory consumption and IP lookup time. Experiments involving binary tries (for simplicity) were conducted on four real-world data sets, two of which were on IPv4 router tables (855,997 and 876,489 active prefixes) and two of which were on IPv6 router tables (155,310 and 157,579 active prefixes). Technologies designed to reduce time taken (at the expense of memory consumption) worked well for IPv4, and those designed to reduce memory consumption worked well for IPv6. Various combinations of these technologies were applied together in the experiments. © 2022 Elsevier Inc.

关键词： Table lookup

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The Impact of Social Media in Marketing on Brand Awareness and Customer Trust in Increasing Purchase Intention

The Impact of Social Media in Marketing on Brand Awareness a...

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2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Fernando, Erick Prabowo, Yulius Denny Winanti Universitas Multimedia Nusantara Faculty of Engineering and Informatics Department of Information Systems Tangerang Indonesia Binus Online Learning Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Universitas Insan Pembangunan Department of Information Systems Indonesia

ISBN: (纸本)9798350376210

In the current digital era, marketing has developed using technology through media to reach consumers. One prominent feature is influencer marketing, where individuals with significant social media influence promote products or services to their audiences. This strategy has become dominant because of its potential to expand brand reach and boost sales. However, there are challenges in ensuring the effectiveness of influencer marketing, particularly in delivering content with sufficient information and entertainment value. Overcoming these challenges is critical to maximizing customer purchase intent. This research explores the influence of information value, entertainment value, brand awareness, and customer trust on customer purchase intentions in influencer marketing. This research used an online survey (google form) with 398 valid respondents used for analysis PLS-SEM with SMART PLS 3.0. The indicator test results are valid and reliable with loading value for each item with a value >0.5, AVE value ⩾ 0.50, Cronbach's alpha value ⩾ 0.70 and Composite Reliability (CR) value ⩾ 0.7. The findings show that these factors have a significant effect on purchase intention. In emphasizing the importance of influencers delivering educational and entertaining content to increase Brand Awareness in building customer trust, with the consistency of influencers. Overall, providing valuable insights for companies looking to optimize their influencer marketing strategy to increase brand awareness and customer trust, ultimately driving customer purchase intent. © 2024 IEEE.

关键词： Sales

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COVID-19 airborne transmission risk calculation using CO2 concentrations in a 3D office environment 17

COVID-19 airborne transmission risk calculation using CO2 co...

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17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022

作者： Kakoulli, Christina Athanasiou, Modestos Kyriacou, Alexis Michaelides, Michalis Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Lelantus Innovations Ltd Nicosia Cyprus

The ongoing COVID-19 pandemic has caused millions of deaths worldwide along with detrimental socioeconomic consequences. Existing evidence suggests that the rate of indoor transmission is directly linked with the Indoor Air Quality (IAQ) conditions. Most of the existing methodologies for virus transmissibility risk estimation are based on the well-known Wells-Riley equation and assume well-mixed, uniform conditions;so spatiotemporal variations within the indoor space are not captured. In this work, a novel fine-grained methodology for real-time virus transmission risk estimation is developed using a 3D model of a real office room with 31 occupants. CONTAM-CFD0 software is used to compute the airflow vectors and the resulting 3D CO2 concentration map (attributed to the exhalations from the occupants). Simulation results are also provided that demonstrate the efficacy of using CO2 sensors for estimating the infection risk in real-time in the 3D office environment. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

关键词： COVID-19

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An Ensemble Deep Learning Approach for Accurate Urinary Sediment Detection Using YOLOv9e and KD-YOLOX-ViT

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IEEE Access 2025年 13卷 101934-101954页

作者： Mansura Naznine Abdus Salam Muhammad Mohsin Khan Sadia Farhana Nobi Muhammad E. H. Chowdhury Department of Electrical Engineering Qatar University Doha Qatar Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Department of Electrical and Computer Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Department of Neurology Hamad Medical Corporation Doha Qatar Department of Public Health and Informatics BSMMU Dhaka Bangladesh

Urinary Tract Infections (UTIs) are common medical diseases that occur from bacterial infections which can impact any area of the urinary system. Detecting urine particles is crucial for diagnosing UTIs and other renal abnormalities, as the presence of red blood cells (RBCs), white blood cells (WBCs), casts, fungi, and bacteria in urine can indicate disorders such as hematuria, kidney stones, and urinary tract malignancies. This study introduces an advanced deep learning method that utilizes ensemble of YOLOv9e and KD-YOLOX-ViT models. YOLOv9e model is built upon the Generalized Efficient Layer Aggregation Network (GELAN) and Programmable Gradient Information (PGI). The KD-YOLOX-ViT model integrates knowledge distillation and Vision Transformer (ViT) modules within the YOLOX framework to improve performance. This ensemble framework combines the strengths of YOLOv9e and KD-YOLOX-ViT models to automate the detection and classification of seven distinct urine sediment particles, effectively addressing challenges related to class imbalance, image resolution, and domain adaptation. The YOLOv9e model exhibited exceptional performance, obtaining precision, recall, and mean average precision (mAP50) scores of 88.5%, 88.1%, and 92.2%, respectively. The KD-YOLOX-ViT model also performed well, achieving precision, recall, mAP50, and mAP50-95 scores of 86%, 88%, 86.7%, and 53.3%, respectively. By ensembling YOLOv9e and KD-YOLOX-ViT using Weighted Box Fusion (WBF), the model achieved a final mAP50 of 94.18%. The model’s adaptability was further validated through rigorous external validation on a novel dataset, yielding in a mAP50 of 94.64%. Comparative analysis with state-of-the-art methods confirms the model’s real-time analytical capabilities. Moreover, the integration of eXplainable AI (XAI) enhances interpretability, offering valuable insights for confident clinical diagnosis. This comprehensive approach shows significant promise in advancing diagnostic accuracy and enabling

关键词： Sediments Accuracy Adaptation models Kidney Deep learning Microscopy White blood cells Medical diagnostic imaging Feature extraction Bacteria

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Self-checking Corner of Upper Acute Respiratory Infection Disease Using Certainty Factor Method

Self-checking Corner of Upper Acute Respiratory Infection Di...

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International Electronics Symposium (IES)

作者： Zulqhi Fahri Muda Arna Fariza Entin Martiana Kusumaningtyas Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

ISBN: (数字)9798350391992

ISBN: (纸本)9798350392005

The Covid-19 pandemic has altered people’s behaviors, particularly those of students. Students prefer health consultations through telemedicine applications, which in 2020 recorded a surge of up to 600%. Telemedicine applications require costs incurred and inefficient time to conduct consultations. Seeing this, it is necessary to have a health-checking application at an institution, especially a campus, which is free of charge, without direct contact and has valid information, and is supported by artificial intelligence. The solution offered in this research provides a Self-Checking Corner application called Self-Heal as a pre-diagnosis of upper Acute Respiratory Infection (ARI) using the expert system method, Certainty Factor. This paper proposes the creation of an application based on the certainty factor method with valid data from experts. It optimizes the categorization and weighting of scalars, so it can be used for pre- diagnosis before the user consults a doctor. The use of data on symptoms, diseases, and rule tables is obtained from experts, namely doctors so that data validation is more accurate. The accuracy of the initial diagnosis of Acute Respiratory Infection (ARI) disease is close to 80% when the doctor diagnoses directly from 3 trials conducted. The application can be as an application for pre-diagnosis of upper respiratory tract diseases that save time and without cost.

关键词： COVID-19 Accuracy Costs Pandemics Telemedicine Medical services Security

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Sentiment Analysis in Indonesian Trading using Lexicon-based and Support Vector Machine

Sentiment Analysis in Indonesian Trading using Lexicon-based...

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2023 International Conference on computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Oetama, Raymond Sunardi Yanfi, Yanfi Ikhsan Assiddiq Up, Masagus M. Muhamad Isa, Sani Universitas Multimedia Nusantara Information System DepartmentFaculty of Engineering and Informatics Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo customers. Since many customers were deceived, this case became quite popular. This study was executed to see how Indonesians felt about the Binomo application after the case went viral. The solution taken was in the form of sentiment analysis because there had been no previous research on sentiment analysis that discussed the Binomo case. The data was scanned using Netlytic tools, a cloud-based text and social network analyzer capable of identifying any talks on social media sites such as Twitter. The sentiment analysis of Binomo trading tweets by using the Multi-Perspective Question Answering lexicon utilized the KNIME tool. But unfortunately, the accuracy of sentiment analysis results is low. Furthermore, the Support Vector Machine technique is also being conducted. The Term Frequency-Inverse Document Frequency method is applied to perform feature extraction whilst the chi-square approach is utilized to identify features that are thought to be useful for inclusion in the classification process and to exclude features that are irrelevant to the target class. The obtained accuracy is 86%. The study proposes that words from the algorithm's outputs can be utilized to improve the quality of sentiment analysis using the lexicon. As an outcome of the algorithm, positive and negative terms are added to the lexicon, increasing the accuracy of sentiment analysis using the new vocabulary from 58.984% to 71.146%. © 2023 IEEE.

关键词： Support vector machines

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The Complexity of Computing Optimum Labelings for Temporal Connectivity 47

The Complexity of Computing Optimum Labelings for Temporal C...

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47th International Symposium on Mathematical Foundations of computer Science, MFCS 2022

作者： Klobas, Nina Mertzios, George B. Molter, Hendrik Spirakis, Paul G. Department of Computer Science Durham University United Kingdom Department of Industrial Engineering and Management Ben-Gurion University of the Negev Israel Department of Computer Science University of Liverpool United Kingdom Computer Engineering & Informatics Department University of Patras Greece

ISBN: (纸本)9783959772563

A graph is temporally connected if there exists a strict temporal path, i.e., a path whose edges have strictly increasing labels, from every vertex u to every other vertex v. In this paper we study temporal design problems for undirected temporally connected graphs. The basic setting of these optimization problems is as follows: given a connected undirected graph G, what is the smallest number |λ| of time-labels that we need to add to the edges of G such that the resulting temporal graph (G, λ) is temporally connected? As it turns out, this basic problem, called Minimum Labeling (ML), can be optimally solved in polynomial time. However, exploiting the temporal dimension, the problem becomes more interesting and meaningful in its following variations, which we investigate in this paper. First we consider the problem Min. Aged Labeling (MAL) of temporally connecting the graph when we are given an upper-bound on the allowed age (i.e., maximum label) of the obtained temporal graph (G, λ). Second we consider the problem Min. Steiner Labeling (MSL), where the aim is now to have a temporal path between any pair of "important" vertices which lie in a subset R ⊆ V , which we call the terminals. This relaxed problem resembles the problem Steiner Tree in static (i.e., non-temporal) graphs. However, due to the requirement of strictly increasing labels in a temporal path, Steiner Tree is not a special case of MSL. Finally we consider the age-restricted version of MSL, namely Min. Aged Steiner Labeling (MASL). Our main results are threefold: we prove that (i) MAL becomes NP-complete on undirected graphs, while (ii) MASL becomes W[1]-hard with respect to the number |R| of terminals. On the other hand we prove that (iii) although the age-unrestricted problem MSL remains NP-hard, it is in FPT with respect to the number |R| of terminals. That is, adding the age restriction, makes the above problems strictly harder (unless P=NP or W[1]=FPT). © 2022 Schloss Dagstuhl- Leibniz-Zentrum fu

关键词： Optimization

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