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1st International Conference on Technology Innovation and Its Applications, ICTIIA 2022

作者： Pratama, Yudhistira Adhitya Iswara, Bagus Ajie Nababan, Adli Abdillah Information System Studies Program Pelita Harapan University Medan Campus Faculty Of Computer Science Medan Indonesia Master's Study Program Informatics Engineering Universitas Sumatera Utara Faculty Of Computer Science And Information Technology Medan Indonesia Digital Business Study Program Stmik Pelita Nusantara Medan Indonesia

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

Widyaiswara is required to show the best performance to fulfill his duties and obligations. Therefore, it is very important to measure the performance of the Widyaiswara, so that it can be used as evaluation material by the BPSDM PROVSU. However, the assessment process that occurs is still not precise and time-consuming. Therefore, to minimize this, a decision support system is needed that can minimize time errors and data processing in decision-making so that the assessment runs well and accurately. This system was built by applying the WASPAS method and the TOPSIS method. Based on the results of tests carried out five times, the results of the WASPAS method processing time resulted in an average processing time of 0.00029791 ms and an average memory usage value of 2.836.44 kb, while the results of the TOPSIS method processing time test resulted in an average processing time - an average of 0.000296428 ms and an average memory usage value of 2.862.34 kb. © 2022 IEEE.

关键词： Decision support systems

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Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions

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Discover Internet of Things 2025年第1期5卷 1-23页

作者： Mazhar, Tehseen khan, Sunawar Shahzad, Tariq khan, Muhammad Amir Saeed, Mamoon M. Awotunde, Joseph Bamidele Hamam, Habib School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science International Islamic University Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Science and Information Technology School Education Department Government of Punjab 31200 Layyah Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized. © The Author(s) 2025.

关键词： Electronic health record

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Lightweight Deep Learning for Human Activity Recognition in Ambient Assisted Living

Lightweight Deep Learning for Human Activity Recognition in ...

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Multimedia University engineering Conference (MECON)

作者： Ahsanul Bari Fahmid Al Farid Md. Tanjil Sarker Sarina Mansor Hezerul Abdul Karim Md Roman Bhuiyan Hasanul Bannah Faculty of Engineering Multimedia University Cyberjaya Malaysia Faculty of Computer Science and Informatics Berlin School of Business and Informatics Berlin Germany

ISBN: (数字)9798331530747

ISBN: (纸本)9798331530754

This Lightweight Deep Learning (LDL) for Multi-View Human Activity Recognition in Ambient Assisted Living Systems can significantly improve the conditions of daily activities for people living with the elderly, disabled, or simply needing assistance. Human Activity Recognition (HAR) is essential for these systems; currently, the task of identifying the wide array of human activities remains complicated. This study introduces an improved deep learning solution for recognizing human activity from multiple viewpoints in AAL environments. By utilizing skeleton data from various perspectives, such as the RHM-HAR-SK dataset, we demonstrate useful improvements in accuracy for activity recognition when compared with single-view methods. Our LeNet and Vision Transformer models have been carefully designed to excel with skeletal data, delivering outstanding performance while keeping computational requirements to a minimum. In this research study also highlights the benefits of multi-view human activity recognition in ambient assisted living and emphasizes the effectiveness of efficient deep learning models in enabling assistive technologies that are real-time and resource friendly. These advancements support more independence and safety by supporting the development of AAL systems that are more sensitive and intelligent.

关键词： Deep learning Ambient assisted living Computational modeling Soft sensors Assistive technologies Transformers Data models Skeleton Human activity recognition Older adults

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The Use of Smart Devices for Assessing the User’s Physiological Status 1

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8th Future Technologies Conference, FTC 2023

作者： Fodor, Kristián Balogh, Zoltán Molnár, György Department of Computer Science Faculty of Natural Sciences and Informatics Constantine the Philosopher University in Nitra Nitra Slovakia Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary

ISBN: (数字)9783031474576

ISBN: (纸本)9783031474569

Currently, there are various technical solutions as well as methods by which the user’s physiological functions can be obtained. From the point of view of the technical solutions, approaches based on the application of sensors (galvanic skin response, heart rate, temperature, etc.) or the use of a camera for capturing the face and its parts are most often used. The article is devoted to the creation of a system for identifying and classifying the emotional state of the user while maintaining non-invasiveness, which includes an IoT-based (Internet of Things) user interface. It deals with the capture of data about the user and the subsequent evaluation of this data with the help of a web application for visualizing and evaluating the obtained data. The research uses a pulse sensor and a galvanic skin response sensor, which are connected to an Arduino and a computer, for data evaluation. The results of the paper include the design of a portable and affordable device that can be non-invasively attached to the human body and monitor the measured values for a long time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Citrus Limon L. Ripeness Identification Using Feature Extraction of Mean RGB, HSV, LBP and K-Nearest Neighbor

Citrus Limon L. Ripeness Identification Using Feature Extrac...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Priyo Utomo, Ade Cahyo Sari, Christy Atika Rachmawanto, Eko Hari Hadi, Heru Pramono Mostafa, Salama A. Al-Dayyeni, Wisam Subhi Talib University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Ada University School of Information Technologies and Engineering Baku Azerbaijan

ISBN: (纸本)9798350339215

Citrus Limon L. (Lemon) is a type of fruit that is currently widely consumed, because it contains abundant vitamin C, fiber, and antioxidants. This fruit have high potential in the agribusiness sector and is widely cultivated by traditional farmers. However, during harvest time, traditional farmers generally still use manual methods using the human eye in distinguishing the maturity level of lemons which is less efficient because it has a low accuracy. Digital image processing is one solution to this problem. In the research on the classification of lemon maturity levels using digital image processing with the feature extraction method of the Mean RGB, HSV, and LBP methods and the K-Nearest Neighbor classification algorithm in this study, a total of 120 lemon images were used which were divided into 80 training image data and 40 image data. testing. The results of model performance measurements in the form of the highest accuracy level in the Mean RGB method of 100%, the highest accuracy in the HSV method of 98%, and the highest accuracy in the LBP method of 82.5%. © 2023 IEEE.

关键词： Citrus fruits

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Advancements in Multi-View Human Activity Recognition for Ambient Assisted Living

Advancements in Multi-View Human Activity Recognition for Am...

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Multimedia University engineering Conference (MECON)

ISBN: (数字)9798331530747

ISBN: (纸本)9798331530754

This Ambient Assisted Living (AAL) uses technology to improve the well-being, autonomy, and security of seniors and disabled individuals. AAL services depend on Human Activity Recognition to detect human behaviors from sensor data. Compared to single-view models, multi-view HAR, which aggregates data from multiple sensors, provides improved accuracy and reliable detection. In this review, we assess recent progress in multi-view HAR for AAL, focusing on the value of lightweight deep learning models. We cover the development of HAR systems, as well as the emergence of multi-view datasets and highly efficient deep learning models that are explicitly designed for AAL scenarios. Furthermore, we address problems such as data synchronization, data protection, and adaptability, as well as possible remedies like sensor fusion and transfer learning. As a result, we also demonstrate the potential of multi-view HAR to increase AAL services and outlining future research trends in providing adaptive, efficient, and privacy-aware systems.

关键词： Deep learning Adaptation models Ambient assisted living Accuracy Reviews Transfer learning Sensor fusion Human activity recognition Synchronization Security

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Optimization Accuracy of CNN Model by Utilizing CLAHE Parameters in Image Classification Problems

Optimization Accuracy of CNN Model by Utilizing CLAHE Parame...

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2023 International Conference on Networking, Electrical engineering, computer science, and Technology, IConNECT 2023

作者： Wanto, Anjar Yuhandri, Yuhandri Okfalisa, Okfalisa Universitas Putra Indonesia Yptk Padang Information Technology Faculty of Computer Science Padang Indonesia Universitas Putra Indonesia Yptk Padang Faculty of Computer Science Department of Information Technology Padang Indonesia Universitas Islam Negeri Sultan Syarif Faculty of Science and Technology Department Informatics Engineering Kasim Riau Indonesia

ISBN: (纸本)9798350331172

This study aims to optimize popular Convolutional Neural Network (CNN) models, such as ResNet50, InceptionV4, VGG19, MobileNetV1, MobileNetV2, MobileNetV3 Small, and MobileNetV3 Large, by utilizing the CLAHE (Contrast Limited Adaptive Histogram Equalization) parameter. This research focuses on the problem of classifying retinal fundus images, which have challenges in analyzing the structure and features of the picture. This study compared the performance of CNN models, which were optimized using the CLAHE method as image pre-processing. The fundus retinal image data used is a publicly available dataset. The optimization process is done by changing the parameters in the CNN model and applying the CLAHE technique to improve image quality before the classification process. The results showed that using the CLAHE parameter significantly contributed to improving the performance of the CNN model in classifying retinal fundus images. Some CNN models optimized with CLAHE produce better accuracy than the unoptimized models. There are also variations in performance between the different models, with some models providing better results in classifying retinal fundus images. This study provides new insights regarding using CLAHE parameters in optimizing the CNN model for retinal fundus image classification problems. The results of this research can be the basis for further development in medical image processing and pattern recognition for more accurate and practical diagnostic applications. © 2023 IEEE.

关键词： Neural network models

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Gender Detection from Hand Palm Images: A PySpark-Driven Approach with VGG19 Feature Extraction and MLP Classification 6

Gender Detection from Hand Palm Images: A PySpark-Driven App...

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6th International Conference on Computing and informatics, ICCI 2024

作者： Henedak, Mayar Tarek Ezzeldin, Ganna Waleed Mohamed, Nada Adel Hamed, Malak Essam ElMasarawi, Sarah Mahmoud Fekry, Shahd Mohamed Yasser, Tawfik Fouad, Khaled M. Fathi, Mostafa Abdelbaky, Ibrahim BMD Program ITCS Nile University Giza Egypt AlProgram ITCS Nile University Giza Egypt Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer Science and Engineering New Mansoura University Egypt Computer Science Program ITCS Nile University Giza Egypt Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha13518 Egypt

ISBN: (纸本)9798350373875

This paper presents a comprehensive methodology for gender detection using hand palm images, leveraging image processing techniques and PySpark for scalable and efficient processing. The approach encompasses a meticulous image preprocessing pipeline, incorporating essential stages like grayscale conversion, the application ofthe Difference of Gaussians (DoG) filter, and adaptive histogram equalization. This approach not only refmes image features but also ensures scalability, accommodating large datasets seamlessly. After preprocessing of hand images, the VGG19 model is employed as a powerful feature extractor, utilizing its pre-trained convolutional layers to capture high-level abstract features from hand palm images. The fmal step was to train and fit the tabular data and extracted features on the Multilayer Perceptron (MLP) model, which is a feedforward neural network for classification tasks in PySpark. The MLP model, with its ability to learn complex patterns, proves effective in accurately predicting gender categories based on the extracted features. Finally, the model results were 94% accuracy for training and 87% testing which indicates that the model is generalizing well to unseen data. Also, another evaluation metrics were used like Precision 0.87, Recall 0.87, Fl Score 0.87 and AUC 0.92. © 2024 IEEE.

关键词： Multilayer neural networks

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Spatial Hotspot Data and Weather for Forest Fire Data Clustering 5

Spatial Hotspot Data and Weather for Forest Fire Data Cluste...

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5th International Conference on Information and Communications Technology, ICOIACT 2022

作者： Mimboro, Prasetyo Kusrini, Kusrini Laksito, Arif Dwi Postgraduate Program Universitas Amikom Yogyakarta Yogyakarta Informatics Engineering Departement Amikom Yogyakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Amikom Yogyakarta Indonesia

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

Prediction of fire forest will be needing several parameters that located on the same location and on the same time frame. This is important to have data on the same location and same time periods, since forest fire mostly triggered by weather or temperature condition on certain area on the certain time. Since there are many parameters involved, the preprocessing will need to made the data have standard structure. The clustering process needed to give a label to the data with five class label, very low risk, low risk, medium risk, high risk, very high risk. Based on the data set, we measurement the best cluster number related with DB index, the results shows that 8 cluster is the best number of the data with DB Index score are 0.8856. © 2022 IEEE.

关键词： Fires

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Evaluation of Deep Convolutional Neural Network-Based Tattoo Detection

Evaluation of Deep Convolutional Neural Network-Based Tattoo...

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Inteligencia Ambiental, Ingeniería de Software y Salud Electrónica y Móvil (AmITIC), V Congreso Internacional en

作者： E. Jiménez-Delgado C. Quesada-López A Méndez-Porras A Lara-Petitdemange J Alfaro-Velasco Graduate Program in Computer Science and Informatics University of Costa Rica Computer Engineering Department Technological Institute of Costa Rica School of Computer Science and Informatics University of Costa Rica

ISBN: (数字)9798350364538

ISBN: (纸本)9798350364545

In this study, two deep learning models for automatic tattoo detection were analyzed; a modified Convolutional Neural Network (CNN) and pre-trained ResNet-50 model. In order to achieve this, ResNet-50 uses transfer learning with fine-tuning. The purpose of this study was to evaluate the accuracy, precision, recall, F1-score, and computational efficiency of the system being considered. To augment the dataset included 1000 photos that were equally divided between those showing tattoos and those that did not show tattoos. A k-fold cross-validation approach was employed in training and testing the models. Although custom CNNs are effective, utilizing pre-trained ones like ResNet-50 can offer even better outcomes. Specifically, ResNet-50 attained a higher accuracy (0.86 compared to 0.79), precision (0.85 versus 0.78), recall (0.91 against 0.86), and F1-score (0.91 vis-a-vis 0.86) as compared to custom CNNs. In selecting these models for examination, two main motivations were considered. The first motivation is to see whether transfer learning with a pre-trained ResNet-50 model does well when compared with a customized CNN designed specifically for tattoo detection. Secondly,the intent of this study is to know what advantages can be derived from each approach and their demerits too. Furthermore, it seeks to determine if transfer learning can provide an alternative in contrast to the common CNN techniques with regards to precision and computational efficiency. In this research, two models will be evaluated in order to answer the question of what is better for tattoo detection: transfer learning or designing custom architectures.

关键词： Training Analytical models Accuracy Computational modeling Transfer learning computer architecture Real-time systems Computational efficiency Convolutional neural networks Tuning

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