Hasil untuk "Electronic computers. Computer science"

Niranjan Srinivas, James Parkin, Georg Seelig et al.

Z. Zhen, Muhammad Zeeshan Malik, A. Khan et al.

The rise in living standards and the continuous development in the global economy led to the depletion of resources and increased waste generation per capita. This waste might posture a significant threat to human health or the environmental matrices (water, air, soil) when inadequately treated, transported, stored, or managed/disposed of. Therefore, effective waste management in an economically viable and environmentally friendly way has become meaningful. Prominent technology is the need of the day for circular economy and sustainable development to reduce the speed of depletion in resources and produce an alternative means for the future demands in the different sectors of science and technology. In order to meet the potential requirements for energy production or producing secondary raw material, solid waste may be the prime source. The activities of living organisms convert waste products in one form or another in which electronic waste (e-waste) is a modern-day problem that is growing by leaps and bounds. The disposal protocols of the e-waste management need to be given proper attention to avoid its hazardous impacts. The e-waste is obtained from any equipment or devices that run by electricity or batteries like laptops, palmtops, computers, televisions, mobile phones, digital video discs (DVD), and many more. E-waste is one of the rapidly growing causes of world pollution today. Plenty of research is available in the scientific literature, which shows different approaches being set up and followed to manage and dispose of waste products. These strategies to manage waste products designed by the states all over the globe revolves around minimal production, authentic techniques for the management of waste produced, reuse and recycling, etc. The virtual survey of the available literature on waste management shows that it lacks specificity regarding the management of waste products parallel to ecological sustainability. The presented review covers the sources, potential environmental impacts, and highlights the importance of waste management strategies to provide the latest and updated knowledge. The review also put forward the countermeasures that need to be taken on national and International levels addressing the sensitive issue of waste management.

Ramin Mousa, Saeed Chamani, Mohammad Morsali et al.

Skin cancer (SC) is a life-threatening disease where early diagnosis is critical for effective treatment and survival. While deep learning (DL) has advanced skin cancer diagnosis (SCD), current methods generally yield suboptimal accuracy and efficiency due to challenges in extracting multiscale features from dermoscopic images and optimizing complex model parameters through efficient exploration of the space of hyperparameters. To address this, we propose an approach integrating late Discrete Wavelet Transform (DWT) with pre-trained convolutional neural networks (CNNs) and swarm-based optimization. The late DWT decomposes CNN-extracted feature maps into low- and high-frequency components to improve the detection of subtle lesion patterns, while a self-attention mechanism further refines this by weighing feature importance, focusing on relevant diagnostic information. To refine hyperparameters, three novel swarm-based optimizers – Modified Gorilla Troops Optimizer (MGTO), Improved Gray Wolf Optimization (IGWO), and Fox Optimization (FOX) – are employed searching the space of the hyperparameters to fine-tune the model for superior performance. In comparison to existing methods, experiments on the ISIC-2016 and ISIC-2017 datasets show enhanced classification performance, obtaining at least a 1% accuracy gain. Thus, the suggested framework offers a reliable and effective way to diagnose skin cancer automatically.

Songpon Pumjam, Sarut Panjan, Tarinee Tonggoed et al.

Surface electromyography (sEMG) is widely used for decoding motion intent in prosthetic control and rehabilitation, yet the impact of external load on sEMG-to-kinematics mapping remains insufficiently characterized, particularly for wrist flexion-extension This pilot study investigates wrist angle estimation (0–90°) under four discrete counter-torque levels (0, 25, 50, and 75 N·cm) using a multilayer perceptron neural network (MLPNN) regressor with mean absolute value (MAV) features. Multi-channel sEMG was acquired from three healthy participants while performing isotonic wrist extension (clockwise) and flexion (counterclockwise) in a constrained single-degree-of-freedom setup with potentiometer-based ground truth. Signals were filtered and normalized, and MAV features were extracted using a 200 ms sliding window with a 20 ms step. Across all load levels, the within-subject models achieved very high accuracy (R² = 0.9946–0.9982) with test MSE of 1.23–3.75 deg²; extension yielded lower error than flexion, and the largest error was observed in flexion at 25 N·cm. Because the cohort is small (n = 3), the movement is highly constrained, and subject-independent validation and embedded implementation were not evaluated, these results should be interpreted as a best-case baseline rather than evidence of deployable rehabilitation performance. Future work should test multi-DoF wrist motion, freer movement conditions, richer feature sets, and subject-independent validation.

Cristian Valero-Abundio, Emilio Sansano-Sansano, Raúl Montoliu et al.

Handling geometric transformations, particularly rotations, remains a challenge in deep learning for computer vision. Standard neural networks lack inherent rotation invariance and typically rely on data augmentation or architectural modifications to improve robustness. Although effective, these approaches increase computational demands, require specialised implementations, or alter network structures, limiting their applicability. This paper introduces General Intensity Direction (GID), a preprocessing method that improves rotation robustness without modifying the network architecture. The method estimates a global orientation for each image and aligns it to a canonical reference frame, allowing standard models to process inputs more consistently across different rotations. Unlike moment-based approaches that extract invariant descriptors, this method directly transforms the image while preserving spatial structure, making it compatible with convolutional networks. Experimental evaluation on the rotated MNIST dataset shows that the proposed method achieves higher accuracy than state-of-the-art rotation-invariant architectures. Additional experiments on the CIFAR-10 dataset, confirm that the method remains effective under more complex conditions.

Ezz El-Din Hemdan, Amged Sayed

In recent years, cutting-edge technologies, such as artificial intelligence (AI), blockchain, and digital twin (DT), have revolutionized the healthcare sector by enhancing public health and treatment quality through precise diagnosis, preventive measures, and real-time care capabilities. Despite these advancements, the massive amount of generated biomedical data puts substantial challenges associated with information security, privacy, and scalability. Applying blockchain in healthcare-based digital twins ensures data integrity, immutability, consistency, and security, making it a critical component in addressing these challenges. Federated learning (FL) has also emerged as a promising AI technique to enhance privacy and enable decentralized data processing. This paper investigates the integration of digital twin concepts with blockchain and FL in the healthcare domain, focusing on their architecture and applications. It also explores platforms and solutions that leverage these technologies for secure and scalable medical implementations. A case study on federated learning for electroencephalogram (EEG) signal classification is presented, demonstrating its potential as a diagnostic tool for brain activity analysis and neurological disorder detection. Finally, we highlight the key challenges, emerging opportunities, and future directions in advancing healthcare digital twins with blockchain and federated learning, paving the way for a more intelligent, secure, and privacy-preserving medical ecosystem.

Andika Fadilla Siagian, Suendri Suendri

Retail businesses, particularly hardware stores, often encounter challenges in order management such as delayed deliveries, inaccurate stock tracking, and limited information transparency factors that hinder operational efficiency and customer satisfaction. This study proposes a web-based order management system utilizing Progressive Web Apps (PWA) technology, developed with the Next.js framework. The Periodic Review System (PRS) method is implemented to calculate reorder points based on actual demand and safety stock levels. System development follows the Waterfall model, with data collected through observation, semi-structured interviews, and literature review. Testing confirms that the application enhances stock accuracy, minimizes delivery delays, supports offline access, and meets SEO performance standards. The implementation significantly improves operational efficiency and holds promise for boosting customer loyalty. The study concludes that PWA-based digital systems are practical, scalable solutions for the MSME sector, with future potential for integration of AI, CRM, and real-time analytics.

Elena Rovenskaya, Alexey Ivanov, Sarah Hathiari et al.

Abstract Economic and social interactions are shifting to digital platforms which grow into vast ecosystems where user engagement creates value for members while ecosystem orchestrators harvest massive revenue. The digital ecosystem business model succeeds by adeptly navigating fast-changing environments, including new technologies and volatile demands, through dynamic innovation in a decentralized decision-making setting. This renders digital platform ecosystems complex adaptive systems. Recognizing that natural ecosystems are a prime example of complex adaptive systems, we propose a systematic hierarchical framework for describing and understanding digital ecosystems, rooted in ecology and evolution. Our framework compares digital ecosystems hosted by societies to natural ecosystems embedded in biomes, products to species, and technologies and elements of business strategy to the genetic makeup of a species. As digital platforms face heightened scrutiny about their socio-economic power and societal value, our approach contributes to the development of deeper understanding and sustainable governance of the digital economy.

Chinmaya Kumar Dehury, Lauri Lovén, Praveen Kumar Donta et al.

Industry demands are growing for hyper-distributed applications that span from the cloud to the edge in domains such as smart manufacturing, transportation, and agriculture. Yet today's solutions struggle to meet these demands due to inherent limitations in scalability, interoperability, and trust. In this article, we introduce HERMES (Heterogeneous Computing Continuum with Resource Monetization, Orchestration, and Semantic) - a novel framework designed to transform connectivity and data utilization across the computing continuum. HERMES establishes an open, seamless, and secure environment where resources, from cloud servers to tiny edge devices, can be orchestrated intelligently, data and services can be monetized in a distributed marketplace, and knowledge is shared through semantic interoperability. By bridging these key facets, HERMES lays a foundation for a new generation of distributed applications that are more efficient, trustworthy, and autonomous.

Nadine Aburumman, Ju-Ling Shih, Cigdem Sengul et al.

This paper presents Microtopia, an interdisciplinary programme designed to broaden participation in computer science (CS) among ethnic minority girls. The programme combined coding with design thinking activities, incorporating Artificial Intelligence (AI), the Internet of Things (IoT), and Robotics as key technologies. Learning activities were formulated around the UN Sustainable Development Goals and the Chinese Five Elements philosophy to support problem-based learning. Pupils were organised into "nations" and engaged in sector-based projects (e.g., healthcare, transportation, fashion, tourism, food, architecture). Using pre- and post-questionnaires, we investigated how socioeconomic and ethnocultural factors influenced pupils' preconceptions of CS, and whether participation in Microtopia shifted their perceptions. Through statistical analysis of the questionnaire data, we identified significant increases in students' confidence, enjoyment, and motivation, particularly when computing was presented as relevant to sustainability and global challenges.

Zhaoheng Guo, T. Driver, S. Beauvarlet et al.

Pump-probe experiments with sub-femtosecond resolution are the key to understanding electronic dynamics in quantum systems. Here we demonstrate the generation and control of sub-femtosecond pulse pairs from a two-colour X-ray free-electron laser (XFEL). By measuring the delay between the two pulses with an angular streaking diagnostic, we characterise the group velocity of the XFEL and demonstrate control of the pulse delay down to 270 as. We demonstrate the application of this technique to a pump-probe measurement in core-excited para-aminophenol. These results demonstrate the ability to perform pump-probe experiments with sub-femtosecond resolution and atomic site specificity.

M. Marques, Alberto Castro, Alberto Castro et al.

Raenald Syaputra, Taghfirul Azhima Yoga Siswa, Wawan Joko Pranoto

Banjir merupakan salah satu bencana alam yang sering terjadi di Indonesia, termasuk di Kota Samarinda dengan 18-33 titik desa terdampak dari tahun 2018-2021. Penggunaan machine learning dalam mengklasifikasi bencana banjir sangat penting untuk memprediksi kejadian di masa mendatang. Beberapa penelitian sebelumnya terkait klasifikasi data banjir dalam 3 tahun terakhir telah dilakukan. Namun, dari beberapa penelitian tersebut memunculkan masalah terkait dengan dataset high dimensional yang dapat menurunkan performa model klasifikasi dan menyebabkan overfitting. Selain itu, masalah lain juga muncul dalam hal imbalance data yang menyebabkan bias terhadap kelas mayoritas dan representasi yang tidak akurat. Oleh karena itu, permasalahan dataset high dimensional dan imbalance data merupakan tantangan spesifik yang harus diatas dalam klasifkasi data banjir Kota Samarinda. Penelitian ini bertujuan mengidentifkasi fitur-fitur yang diperoleh dari seleksi fitur Genetic Algorithm (GA) yang memiliki pengaruh terhadap akurasi klasifikasi data banjir Kota Samarinda menggunakan algoritma Support Vector Machine (SVM), serta meningkatkan akurasi klasifikasi data banjir di Kota Samarinda dengan mengimplementasikan algoritma SVM yang dikombinasikan dengan metode Synthetic Minority Oversampling Technique (SMOTE) untuk oversampling, seleksi fitur dengan GA dan optimasi menggunakan Particle Swarm Optimization (PSO). Teknik validasi yang digunakan adalah 10-fold cross validation dan evaluasi performa menggunakan confusion matrix. Data yang digunakan berasal dari BPBD (Badan Penanggulangan Bencana Daerah) dan BMKG (Badan Meteorologi, Klimatologi, dan Geofisika) Kota Samarinda pada tahun 2021-2023 terdiri dari 11 fitur dan 1.095 record. Hasil penelitian menunjukkan bahwa fitur-fitur penting yang terpilih melalui GA adalah temperatur maksimum, kecepatan angin maksimum, arah angin maksimum, arah angin terbanyak, lamanya penyinaran matahari dan kecepatan angin rata-rata. Dengan kombinasi metode SVM, SMOTE, GA dan PSO, akurasi klasifikasi data banjir mencapai 82,28%. Namun, penelitian ini juga menghadapi tantangan seperti kontradiksi hasil dengan penelitian lain terkait penggunaan SMOTE dan variasi hasil akibat karakteristik dataset serta metode pembagian data yang berbeda. Hasil penelitian ini dapat digunakan oleh pemerintah daerah dan badan penanggulangan bencana daerah Kota Samarinda untuk memprediksi kejadian banjir dengan lebih akurat, serta memungkinkan tindakan pencegahan yang lebih efektif. Penerapan hasil penelitian ini dapat meningkatkan efektivitas dalam mitigasi bencana banjir Kota Samarinda.

Nicola Guarino

Tianqi WANG, Yingzhou ZHANG, Yunlong DI, Dingwen LI, Linlin ZHU

A surge in the amount of information comes with the rapid development of the technology industry.Across all industries, there is a need to collect and utilize vast amounts of data.While this big data holds immense value, it also poses unprecedented challenges to the field of data security.As relational databases serve as a fundamental storage medium for data, they often contain large-scale data rich in content and privacy.In the event of a data leak, significant losses may occur, highlighting the pressing need to safeguard database ownership and verify data ownership.However, existing database watermarking technologies face an inherent tradeoff between improving watermark embedding capacity and reducing data distortion.To address this issue and enhance watermark robustness, a novel robust database watermarking algorithm based on dynamic difference expansion was introduced.The QR code was employed as the watermark, the SVD decomposition of the low frequency part of the image was utilized after Haar wavelet transform.By extracting specific feature values and using residual feature values as the watermark sequence, it was ensured that the same-length watermark sequence contains more information and the embedded watermark length can be reduced.Furthermore, by combining the adaptive differential evolution algorithm and the minimum difference algorithm, the optimal embedding attribute bits were selected to alleviate the problems of low computational efficiency, high data distortion and poor robustness of traditional difference expansion techniques in embedding watermarks, and to improve the embedding capacity of watermarks while reducing the distortion of data.Experimental results demonstrate that the proposed algorithm achieves a high watermark embedding rate with low data distortion.It is resilient against multiple attacks, exhibiting excellent robustness and strong traceability.Compared to existing algorithms, it offers distinct advantages and holds great potential for broad application in the field of data security.

Ghazal Kalhor, Tanin Zeraati, Behnam Bahrak

Although different organizations have defined policies towards diversity in academia, many argue that minorities are still disadvantaged in university admissions due to biases. Extensive research has been conducted on detecting partiality patterns in the academic community. However, in the last few decades, limited research has focused on assessing gender and nationality biases in graduate admission results of universities. In this study, we collected a novel and comprehensive dataset containing information on approximately 14,000 graduate students majoring in computer science (CS) at the top 25 North American universities. We used statistical hypothesis tests to determine whether there is a preference for students' gender and nationality in the admission processes. In addition to partiality patterns, we discuss the relationship between gender/nationality diversity and the scientific achievements of research teams. Consistent with previous studies, our findings show that there is no gender bias in the admission of graduate students to research groups, but we observed bias based on students' nationality.

Tai Le Quy, Gunnar Friege, Eirini Ntoutsi

Ensuring fairness is essential for every education system. Machine learning is increasingly supporting the education system and educational data science (EDS) domain, from decision support to educational activities and learning analytics. However, the machine learning-based decisions can be biased because the algorithms may generate the results based on students' protected attributes such as race or gender. Clustering is an important machine learning technique to explore student data in order to support the decision-maker, as well as support educational activities, such as group assignments. Therefore, ensuring high-quality clustering models along with satisfying fairness constraints are important requirements. This chapter comprehensively surveys clustering models and their fairness in EDS. We especially focus on investigating the fair clustering models applied in educational activities. These models are believed to be practical tools for analyzing students' data and ensuring fairness in EDS.

Frederick Choi, Sajjadur Rahman, Hannah Kim et al.

Data science workflows are human-centered processes involving on-demand programming and analysis. While programmable and interactive interfaces such as widgets embedded within computational notebooks are suitable for these workflows, they lack robust state management capabilities and do not support user-defined customization of the interactive components. The absence of such capabilities hinders workflow reusability and transparency while limiting the scope of exploration of the end-users. In response, we developed MAGNETON, a framework for authoring interactive widgets within computational notebooks that enables transparent, reusable, and customizable data science workflows. The framework enhances existing widgets to support fine-grained interaction history management, reusable states, and user-defined customizations. We conducted three case studies in a real-world knowledge graph construction and serving platform to evaluate the effectiveness of these widgets. Based on the observations, we discuss future implications of employing MAGNETON widgets for general-purpose data science workflows.

Jian-Feng Cai, Bin Dong, S. Osher et al.

Chiehyeon Lim, Gi-Hyoug Cho, Jeongseob Kim

Abstract There have been many attempts to transform cities into smart cities worldwide. However, it is difficult to understand and describe smart cities from different perspectives, given the widespread application of the concept of smart city in diverse disciplines, such as urban planning, electronic engineering, and computer sciences. This work conducted a comprehensive smart city literature review based on text mining of 3,315 papers on smart cities published in journals indexed in the Science Citation Index Expanded and Social Sciences Citation Index databases. These include “all papers” classified as research articles published from 1999 to April 2020. Our findings show the state of the art of research on smart cities, including (i) smart city literature statistics from 1999 to 2019, (ii) 23 research topics related to smart cities, and (iii) geographical variations in smart-city research. Based on these findings, we offer theoretical and practical implications of (1) missing fields of studies, (2) future research directions, and (3) the applicability of text-mining techniques to literature reviews. We believe that this work, which aims to establish a common ground for understanding smart cities from multiple disciplinary perspectives, will encourage further research and development regarding smart cities.