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Classification of Paddy Leaf Disease Using MobileNet Model

Classification of Paddy Leaf Disease Using MobileNet Model

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International Conference on Computing, engineering, and Design (ICCED)

作者： Fauzan Masykur Kusworo Adi Oky Dwi Nurhayati Doctoral Program Of Information System School Of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Informatics Engineering Muhammadiyah University of Ponorogo Ponorogo Indonesia

ISBN: (纸本)9781665453905

Paddy is a rice-producing plant as a staple food raw material for most of the population in Indonesia. The rice planting process cannot be separated from pests and diseases that can cause crop failure and impact on rice production stocks. Currently, the process of monitoring the development of rice plants is done conventionally by direct observation by farmers. Meanwhile, in this paper, a classification model and determination of object detection in rice plants infected with pests and diseases is proposed based on leaf color. Leaf classification applies convolution conditional network algorithm with mobilenet as the architecture. The leaf image dataset was taken from public data from *** by involving 4 classes, namely healthy leaf images, brownspot, hispa and leafblast images. In each class the leaf images are grouped into 2 groups, namely 70% training and 30% validation. On the mobilenet backbone, it produces 97% accuracy and 22% loss with the time required for 195s 19s/step in 1 epoch round.

关键词： Training Image color analysis Sociology Computer architecture Production Object detection Raw materials

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Monitoring system for Elderly Health Care Using Smart Band, Raspberry Pi, and Node-Red 2

Monitoring System for Elderly Health Care Using Smart Band, ...

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2nd International Conference on Electronic and Electrical engineering and Intelligent system, ICE3IS 2022

作者： Ardiyanto, Yudhi Adi, Kusworo Kurnianingsih Putra, Karisma Trinanda Utomo, Pradityo System School of Postgraduate Studies Universitas Diponegoro Doctoral Program of Information Semarang Indonesia Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Yogyakarta Indonesia Universitas Diponegoro Faculty of Science and Mathematics Department of Physics Semarang Indonesia Politeknik Negeri Semarang Faculty of Engineering Department of Electrical Engineering Semarang Indonesia Universitas Merdeka Madiun Faculty of Engineering Department of Informatics Management Madiun Indonesia

ISBN: (纸本)9781665465410

The decline in physical, cognitive, and sensory functions for most elderly people leads to the development of several health issues such as memory loss, a tendency to fall, dizziness, and reduced ability to care for oneself independently. These can be mitigated using devices designed to monitor vital signs and human activity such as a smart band. However, the application of a smart band is characterized by a disadvantage which involves storing user data in the vendor's cloud and only one device can access the client applications. Therefore, nurses, doctors, and family members cannot simultaneously analyze data and receive alerts when abnormal conditions occur in elderly patients. This study aims to develop a system capable of monitoring the health conditions of multiple elderly individuals and sending alerts during abnormal conditions based on the heart rate value read by the smart band sensor. The process involved installing a database on the Raspberry Pi to store data while a monitoring system was built on the Node-RED platform to display elderly health data and made it accessible locally. The detection of an abnormal heart rate condition in the elderly patient leads the system to send notifications via telegram to multiple receivers registered in the system using their telegram identity. © 2022 IEEE.

关键词： Heart

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Simulation studies on dynamic and distributed domain-based mobile IPv6 mobility management

Simulation studies on dynamic and distributed domain-based m...

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IEEE International Conference on Networks

作者： Chang Woo Pyo Jie Li H. Kameda Doctoral Program System & Information Engineering University of Tsukuba Tsukuba Science City Ibaraki Japan Institute of Information Science & Elelctronics University of Tsukuba Tsukuba Science City Ibaraki Japan

ISBN: (纸本)0780377885

We address mobility management for the Internet protocol version 6 in mobile networks, namely mobile IPv6 (MIPv6). To correctly transfer IPv6 packets to a mobile node (MN), the MN has to update its binding to the home network while roaming away from its home network. Mobile IP networks suffer high binding update traffic from high rates of handoffs and the long packet delivery delay from the large distance to the home network. In this paper, we introduce a dynamic and distributed mobility management protocol for MIPv 6 to reduce the binding update and packet delivery delay and traffic. We describe the basic protocol operations, and examine the performance improvement of our mobility management against the previous mobile IP mobility management methods. We show that the dynamic and distributed mobility management method outperforms other previous methods, and the dynamic approach provides the optimal mobility solution for MIPv6 and the distributed approach can decentralize the network load.

关键词： Mobile radio mobility management Home automation Cities and towns Telecommunication traffic Protocols systems engineering and theory information science IP networks Mobile communication Internet

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Optimizing Student Performance with GridSearchCV Using Random Forest Classifier Method to Enhance Learning Outcome Prediction

Optimizing Student Performance with GridSearchCV Using Rando...

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International Conference on Informatics and Computing (ICIC)

作者： Riza Arifudin Rizal Isnanto Budi Warsito Doctoral Program of Information System Diponegoro University Semarang Indonesia Departementof Computer Engineering Diponegoro University Semarang Indonesia Department of Statistics Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Predicting student performance is becoming increasingly important because it plays a major role in the sustainable development of countries, which depend on effective education systems to produce a generation of leaders in various aspects of life, including science, economy, social, and military. Advances in educational technology have changed academic performance analysis, allowing for more accurate and databased predictions of student performance This research aims to optimize the performance of student learning outcome prediction by using the Random Forest Classifier method which is improved through the GridSearchCV technique. This research focuses on improving accuracy and efficiency in predicting student learning outcomes based on historical data and variables used. The data used in this study were collected from various educational sources that included student academic achievement records, demographic data, and other related variables. The Random Forest Classifier method is applied as the main prediction model. The GridSearchCV technique is used to find optimal combinations of parameters that improve the model’s performance. Model evaluation is carried out using accuracy, precision, recall, and F1-score metrics to ensure the reliability of predictions. The results show that the use of GridSearchCV significantly improves the performance of the Random Forest Classifier model. The optimized model is able to achieve a higher prediction accuracy of 0.89. This improvement was also seen in other evaluation metrics, which showed an overall improvement in the model’s predictive ability to student learning outcomes.

关键词： Measurement Accuracy Educational technology Predictive models Performance analysis Reliability Sustainable development Informatics Random forests Faces

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Intrusion Detection using Deep Neural Network Algorithm on the Internet of Things 11

Intrusion Detection using Deep Neural Network Algorithm on t...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Ikhwan, Syariful Wibowo, Adi Warsito, Budi School of Postgraduate Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Institut Teknologi Telkom Purwokerto D3 Telecommunication Engineering Banyumas Indonesia Diponegoro University Computer Science Informatics Department of Science and Mathematics Faculty Semarang Indonesia Diponegoro University Statistics Department of Science and Mathematics Faculty Semarang Indonesia

ISBN: (纸本)9781665460309

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small. © 2022 IEEE.

关键词： Internet of things

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Deep Learning Methods for Plankton Identification: A Bibliometric Analysis and General Review 1

Deep Learning Methods for Plankton Identification: A Bibliom...

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1st International Conference on Smart Technology, Applied Informatics, and engineering, APICS 2022

作者： Ardhi, Ovide Decroly Wisnu Soeprobowati, Tri Retnaningsih Adi, Kusworo Prakasa, Esa Rachman, Arief School of Postgraduate Studies Universitas Diponegoro Doctoral of Information System Program Semarang Indonesia Vocational School Universitas Sebelas Maret Department of Informatics Engineering Surakarta Indonesia Universitas Diponegoro Faculty of Science of Mathematics Department of Biology Semarang Indonesia Universitas Diponegoro Faculty of Science of Mathematics Department of Physics Semarang Indonesia Bandung Indonesia Jakarta Indonesia

ISBN: (数字)9781665489850

ISBN: (纸本)9781665489850

This study investigates trends in deep learning research on microscopic images to identify and classify plankton. We performed a bibliometric analysis using VOSviewer to display the visualizations. The first step is to extract the relevant journals from the Scopus database. Extraction from relevant journals found 42 articles, including 21 journals and 21 conferences. We analyzed publication trends and analyzes of co-authorship, co-occurrence, and citations. In addition, it examines the collaborative relationship between authors by country, the emergence of co-author keywords, and the citation of journal sources. We found that the researchers performed plankton classification tasks that achieved high accuracy (>90%). The plankton identification method uses a deep learning architecture based on the Convolutional Neural Network. Among the architectures are AlexNet, GoogleNet, InceptionV3, VGGNet (16.19), ResNet (50.101), DenseNet, NasNet, MobileNetV2, ResidualNet, Shufflenet, EfficienNet, and CapsNet. Some researchers combine CNN with SparseConvNets, LMP, SVM, MorphoCluster, and Quantification Algorithm (CC, AC, PCC, PAC, Hdy). Some methods also implement transfer learning, fine-tuning, and advanced features. In addition to the CNN approach, End-to-End Object Detection with transformers is one of the new approaches in the future to identify plankton. © 2022 IEEE.

关键词： Deep learning

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Exploring Low Rate Dos Attack Detection Methods: A Bibliometric Analysis and Roadmap for Further Research

Exploring Low Rate Dos Attack Detection Methods: A Bibliomet...

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International Conference on Data and Software engineering (ICODSE)

作者： Anteng Widodo Adian Fatchur Rochim Budi Warsito Doctoral Program of Information System School of Postgraduated Studies Diponegoro University Semarang Indonesia Department of Information System Faculty of Engineering Muria Kudus University Kudus Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Statistics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

Low-Rate Denial of Service (LDoS) attacks, an emerging breed of DoS attacks, present a formidable challenge in terms of their detectability. Within the realm of network security, these attacks cast a substantial shadow over the availability and integrity of services. This study seeks to delve into the intricate mechanics of LDoS attacks and explore the landscape of developed detection methodologies. Employing bibliometric analysis, we employ a multifaceted approach to gather research insights pertaining to LDoS attack detection. This approach encompasses the examination of evolving research trends, scrutiny of widely cited scientific publications within the same domain, identification of prolific researchers shaping the field, and a judicious selection of reputable publishers for dependable information acquisition. Furthermore, our study undertakes a meticulous categorization and classification of previously proposed LDoS detection methods documented in well-established scientific literature. This endeavor not only provides a more profound comprehension of the evolutionary trajectory and thematic thrusts within LDoS attack detection research, but it also facilitates the identification of gaps and directions for future exploration. The culmination of these analytical efforts culminates in a comprehensive framework. This contribution manifests through structured categorization, classification, and a forward-looking research roadmap, which collectively serve as valuable tools for both current and aspiring researchers engaged in LDoS attack detection. In summary, this research not only delves into the complexities of LDoS attacks and their mechanisms but also augments the current understanding of detection capabilities. Through its nuanced contributions, this paper extends a helpful guide to the dynamic realm of LDoS attack detection, thus catalyzing advancements and insights for the future.

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Initial Study of Oil Palm Crop Condition Detection Through Drone Imagery: A YOLO Approach

Initial Study of Oil Palm Crop Condition Detection Through D...

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International Conference on Informatics, Multimedia, Cyber and information system (ICIMCIS)

作者： Winata Nugraha Ermatita Deris Stiawan Faculty of Computer Science Doctoral Program Faculty of Engineering Universitas Sriwijaya Palembang Indonesia Department of Information System Universitas Sriwijaya Palembang Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

Oil palm plantations in Indonesia still have many challenges, especially in terms of monitoring and mapping technology. One of the important aspects in the monitoring stage of oil palm plantations is monitoring the productivity of oil palm plants. To identify the oil palm plants, many previous studies have utilized image analysis to solve the problem. The problem is that poor image conditions can affect the architecture model to be built.[1] So in this research we do tune mapping on the image to produce a quality image. Each image data that has been improved will be feature extracted using ResNet and VGG to produce the best model. Furthermore, the classification model development process is carried out using CNN architecture as the model architecture. We also provide innovation by using object detection technology. Which uses artificial neural networks. Here we train the model using aerial video datasets of oil palm plants obtained from drones. Furthermore, to evaluate the performance of the above technique, it is proposed to use the mapping model, precision and accuracy evaluation obtained in the test phase[2]

关键词： YOLO Productivity Technological innovation Oils Multimedia systems Plantations Feature extraction Informatics Monitoring Drones

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Privacy Preserving Electronic Health Record with Consortium Blockchain

Privacy Preserving Electronic Health Record with Consortium ...

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International Conference on information Technology, information systems and Electrical engineering (ICITISEE)

作者： Adri Gautama Adian Fatchur Rochim Luhur Bayuaji Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Computer Engineering Diponegoro University Semarang Indonesia Computer Science Budi Luhur University Jakarta Indonesia

Every patient has health record, it was written as statement of patient's conditions, treatments, and medications, and nowadays it is become digitalized, it can be copied and shared easily, but the nature of EHR is private, not for public, private between patients and healthcare provider. Privacy preserving can be done by implementing Blockchain network for storing EHR, only authorized users or nodes can access and write data to the network. New block in blockchain created as per consensus among nodes, every node can join a blockchain network if it is public, to meet a consensus required resources intensive cryptographic challenge, in private blockchain, only selected can join and involved in the network or specific transaction. In healthcare, data privacy is the main concerned, need a secure, scalable, and efficient blockchain hence implementing consortium blockchain is perfect match for EHR where multiple healthcare provider can operate in a single platform to conduct transaction or sharing EHR among members. This study analyzes 15 articles derived from IEEE Explore, Science Direct and Scopus by implementing screening with keyword Consortium Blockchain and EHR and proposing new blockchain model accommodating solution for challenges on access, storage, sharing, privacy, and performance.

关键词： Electrical engineering Data privacy Privacy Analytical models Monte Carlo methods Medical services Blockchains

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Bibliometric Analysis of Trends and Directions in Machine Learning Applied to FinTech

Bibliometric Analysis of Trends and Directions in Machine Le...

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Electronic and Electrical engineering and Intelligent system (ICE3IS), International Conference on

作者： Erman Arif Suherman Suherman Aris Puji Widodo Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

ISBN: (数字)9798350378368

ISBN: (纸本)9798350378375

Our article presents a comprehensive analysis of trends and directions in the application of machine learning in the FinTech sector. By utilizing data from academic databases such as Scopus, our objective is to showcase the advancements in this interdisciplinary research area. The study identifies key trends, influential authors, leading journals, and significant collaborative networks that shape the field. The methodology employed involves quantitative bibliometric analysis to evaluate publications from the past five years. The results indicate a substantial increase in the number of studies during this period, highlighting the importance of machine learning in addressing critical financial challenges, including fraud detection, algorithmic trading, credit scoring, and risk management. Additionally, we delve into the main themes, countries, and institutions that dominate the current research agenda. This analysis provides valuable insights into the present state and future direction of machine learning in FinTech, serving as a resource for researchers and practitioners seeking to comprehend and contribute to this dynamic and rapidly evolving field. We also discuss implications for future research, emphasizing the necessity for continuous innovation and interdisciplinary collaboration to maximize the potential of machine learning in transforming financial services.

关键词： Symbiosis Technological innovation Machine learning algorithms Bibliometrics Collaboration Machine learning Market research Dynamic scheduling Fraud Risk management

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