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"Expanding the frontiers of visual analytics and visualization provides a review of the state of the art in computer graphics, visualization, and visual analytics by researchers and developers who are closely involved in pioneering the latest advances in the field. It is a unique presentation of multi-disciplinary aspects in visualization and visual analytics, architecture and displays, augmented reality, the use of color, user interfaces and cognitive aspects, and technology transfer. It provides readers with insights into the latest developments in areas such as new displays and new display processors, new collaboration technologies, the role of visual, multimedia, and multimodal user interfaces, visual analysis at extreme scale, and adaptive visualization."

"Museum anak merupakan salah satu wadah nonformal bagi anak untuk mendapatkan informasi. Dalam proses penyampaian informasi tersebut, diperlukan media komunikasi yang tepat berupa elemen visual yang sesuai dengan tahapan perkembangan anak sebagai pengunjung museum. Penerapan elemen visual yang sesuai dengan tahapan perkembangan kognitif anak menjadi faktor utama dalam menarik perhatian anak untuk dapat belajar dan mengolah informasi dengan maksimal. Pada tulisan ini, penulis memfokuskan pembahasan pada elemen visual dalam museum anak berupa warna, bentuk, grafis, dan teks yang baik digunakan untuk anak yang berada pada tahap praoperasional dan operasional konkret. Studi kasus dilakukan pada Museum PP-IPTEK dan ruang pamer Create! Hopscotch for Geniuses oleh teamLab yang merupakan ruang pamer yang menargetkan anak sebagai pengunjung utamanya. Pengambilan data untuk melengkapi kajian ini dilakukan dengan cara observasi dan wawancara langsung ke Museum IPTEK dan melalui laman resmi ruang pamer Create! Hopscotch for Geniuses oleh teamLab. Kedua museum atau ruang pamer tersebut kemudian dikaji berdasarkan literatur kemudian dikomparasikan. Hasil dari analisis menunjukkan bahwa elemen visual berupa warna primer, bentuk geometri dasar, grafis yang menyenangkan serta familiar bagi anak, dan teks dengan ukuran besar dan bentuk bulat merupakan jenis media komunikasi yang sesuai dengan tahapan perkembangan anak tahap praoperasional dan operasional konkret.

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Children's Museum is a non-formal forum for children to get information. In the process of delivering this information, it is necessary to have the right communication media in the form of visual elements that are in accordance with the stages of child development as museum visitors. The application of visual elements in accordance with the stages of children's cognitive development is the main factor in attracting children's attention to be able to learn and process information optimally. In this paper, the author focuses on the discussion of visual elements in the children's museum in the form of colors, shapes, graphics, and text that are good for children who are in the preoperational and concrete operational stages. The case study was conducted at the PP-IPTEK Museum and the Create! Hopscotch for Geniuses by teamLab which is an exhibition that targets children as its main visitors. Data collection to complete this study was carried out by direct observation and interviews to the Science and Technology Museum and through the official website of the Create! Hopscotch for Geniuses by teamLab. The two museums or exhibition rooms are then reviewed based on the literature and then compared. The results of the analysis show that visual elements in the form of primary colors, basic geometric shapes, graphics that are fun and familiar to children, and text with large sizes and round shapes are types of communication media that are suitable for the preoperational and concrete operational stages of child development. "

"Alzheimer’s Disease (AD) merupakan penyakit degeneratif pada organ otak yang dapat menyebabkan penurunan kognitif, sehingga mempengaruhi kualitas hidup dan perilaku penderita. Diagnosis secara dini dan akurat dari AD, termasuk mengindentifikasi tahap awal dari penyakit ini yang disebut sebagai mild cognitive impairment (MCI), merupakan hal yang penting untuk melakukan intervensi terhadap penyakit ini. Fluorodeoxyglucose Positrons Emission Tomography (FDG-PET) adalah modalitas pencitraan molekuler fungsional yang dapat digunakan untuk membantu memahami perubahan secara anatomis dan perubahan secara neural pada otak yang berhubungan dengan AD. Keberhasilan model Convolutional Neural Network (CNN) terutama dalam klasifikasi citra, kami mengembangkan sebual model CNN yang dilatih menggunakan data citra dari FDG-PET dari Alzheimer’s Disease Neuroimaging Initiative (ADNI) untuk dapat mengklasifikasikan antara citra dengan kondisi AD, MCI, dan Cognitive Normal (CN). Metode Gradient-weighted Class Activation Mapping (Grad-CAM) digunakan untuk meningkatkan interpretabiltas model, menunjukkan daerah-daerah penting dari prediksi dan membuat model menjadi lebih transparan dan dapat dijelaskan. Model multiclass yang dikembangkan memperoleh akurasi (97%), presisi (99%), recall (99%), dan F1-Score (99%). Heatmap yang dihasilkan dari Grad-CAM memberikan informasi visual terhadap proses pembuatan keputusan dari model, yang dapat membantu dalam memahami daerah-daerah penting pada citra yang diklasifikasi. Berdasarkan penelitan ini, model yang dikembangkan selain memiliki kapabilitas dalam melakukan klasifikasi penyakit Alzheimer serta tahap awal dari penyakit, model juga memberikan informasi visual yang berpotensi untuk memajukan perangkat diagnostik dalam bidang kesehatan.

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Alzheimer’s disease (AD) is a brain degenerative disorder can cause cognitive decline, impacting daily life and behaviour of the patient. Early and accurate diagnosis of AD, including identifying its prodromal stage (mild cognitive impairment (MCI), is crucial for effective intervention of the disease. Fluorodeoxyglucose Positrons Emission Tomography (FDG-PET) is a functional molecular imaging modality, that can be used to help understand the anatomical and neural changes of brain related to AD. With the success of Convolutional Neural Network (CNN) especially in image classification, we develop a CNN model trained on FDG-PET images from Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset to discern between AD, MCI, and Cognitive Normal (CN) states. Incorporating Gradient-weighted Class Activation Mapping (Grad-CAM) enhances model interpretability, highlighting crucial image regions for disease prediction and to enhance our model interpretability and make our model more transparent and explainable. Our multiclass model achieves accuracy (97%), precision (99%), recall (99%), and F1 scores (99%). Grad-CAM-generated heatmaps give insight into the model’s decision-making process, aiding in understanding important classification regions within images. Based on our findings, not only demonstrate the model’s capability in classifying Alzheimer’s disease and its prodromal stage but it also provides visual insights that showcase its potential to advance diagnostic tools in healthcare."

"In this book, Graham Wills bridges the gap between the art and the science of visually representing data. He does not simply give rules and advice, but bases these on general principles and provide a clear path between them. "

"Many important planning decisions in society and business depend on proper knowledge and a correct understanding of movement, be it in transportation, logistics, biology, or the life sciences. Today the widespread use of mobile phones and technologies like GPS and RFID provides an immense amount of data on location and movement. What is needed are new methods of visualization and algorithmic data analysis that are tightly integrated and complement each other to allow end-users and analysts to extract useful knowledge from these extremely large data volumes. This is exactly the topic of this"