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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Delnevo, Giovanni Olaiya, Kelvin Salomoni, Paola University of Bologna Dept. of Computer Science and Engineering Bologna Italy

ISBN: (纸本)9798331508050

This paper explores the evolving concept of digital sustainability, emphasizing its growing importance as digital technologies become essential to addressing global sustainability challenges. Through a thematic literature review focused on computer science, we analyze papers from Scopus-indexed journals to identify key themes in the discourse on digital sustainability. Additionally, we map the identified topics to the traditional three-pillar model of sustainable development, noting areas of alignment and divergence. The study offers insights into how digital sustainability is framed within the computer science literature and its implications for future research. © 2025 IEEE.

关键词： Green development

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Challenges in Numerical Simulation of Frost Heave

Challenges in Numerical Simulation of Frost Heave

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Geotechnical Frontiers 2025: Soil Characterization and Improvement

作者： Dong, Antai Zhang, Xiong Dept. of Civil Architectural and Environmental Engineering Missouri Univ. of Science and Technology RollaMO United States

ISBN: (数字)9780784486016

Frost heave of soil extensively exists in northern regions and poses a significant threat to infrastructure in cold regions. Despite over a century of research, challenges persist in numerically simulating frost heave. This study addresses two key issues: (1) What is the primary driving force for liquid water transfer during the freezing process? (2) How can we correctly represent unfrozen water content? Critical insights are derived from the theoretical analysis of coupled hydrothermal migration during soil freezing processes, followed by a case simulation using COMSOL Multiphysics. It concludes that of the water content gradient, suction gradient, and hydraulic gradient, only the hydraulic gradient is the fundamental driving force for liquid water flow. Moreover, unfrozen water content is an intrinsic property of frozen soil and should not be indirectly determined by assessing ice content. This study enhances our understanding of the frost heave mechanism and contributes to developing a unified model for frost heave. © ASCE.

关键词： Water content

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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A 200/400 GHz Cascode SiGe Voltage-Controlled Oscillator Exploiting Harmonic Optimization Technique for mmWave Applications

A 200/400 GHz Cascode SiGe Voltage-Controlled Oscillator Exp...

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2025 International Workshop on Integrated Nonlinear Microwave and Millimetre-Wave Circuits, INMMiC 2025

作者： Khanehbeygi, Mehran Tatangelo, Giorgio Pantoli, Leonardo Leuzzi, Giorgio University of L'Aquila Dept. of Industrial and Information Engineering & Economics L'Aquila Italy University of L'Aquila Computer Science and Mathematics Dept. of Information Engineering L'Aquila Italy

ISBN: (纸本)9798331519605

This brief presents a Voltage-Controlled Oscillator (VCO) for operation in the mmWave frequency range. The VCO is based on the Colpitts topology, utilising SiGe:C 130 nm bipolar devices. Unlike traditional configurations, the proposed VCO simultaneously provides both a differential output at the fundamental frequency (200 GHz) and a push-push output at the second harmonic (400 GHz). The overall performance is significantly enhanced through the implementation of an innovative Second Harmonic Extraction Network (SHEN), which efficiently captures and utilizes the second harmonic for improved output power and efficiency. © 2025 IEEE.

关键词： Colpitts MMIC mmWave Oscillator SiGe

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Numerical Modeling of Contributing Factors to Rainfall-Induced Slope Failure: New Jersey Case Study

Numerical Modeling of Contributing Factors to Rainfall-Induc...

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Geotechnical Frontiers 2025: Foundations, Retaining Structures, and Geosynthetics

作者： Khadka, Prabhakar Kolawole, Oladoyin Amenuvor, Andrew C. Ankah, Mawuko L.Y. Dept. of Civil and Environmental Engineering New Jersey Institute of Technology NewarkNJ United States Dept. of Geological Engineering Kwame Nkrumah Univ. of Science and Technology Kumasi Ghana Dept. of Geological Engineering Univ. of Mines and Technology Tarkwa Ghana

ISBN: (数字)9780784485972

Rainfall is a key factor triggering slope failures, leading to geohazards and affecting civil infrastructure. Traditional slope stability assessments use dry-state analysis and groundwater table models, which may be insufficient for predicting the Factor of Safety (FoS) under rainfall conditions. This study investigated the governing factors that can induce slope failure due to rainfall infiltration in non-tropical areas, focusing on a previously failed slope in Northern New Jersey. A two-dimensional Limit Equilibrium Method (LEM) and Finite Element Method (FEM) were employed. Initial dry-state and subsequent post-rainfall slope stability analyses were conducted. The results indicated a reduction in FoS due to rainfall (LEM: 1.59 to 1.46, FEM: 1.49 to 1.11), attributed to reduced matric suction and increased soil saturation. Further analysis revealed a 6%-25.5% FoS decrease, with shear strain (+139%) and displacement (+95%) increasing. The findings suggest that rising rainfall events may destabilize slopes, necessitating slope protection work for long-term stability. © ASCE.

关键词： Slope stability

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An Overview of Machine Learning Applications in Blockchain for Green Energy Exchanges 33

An Overview of Machine Learning Applications in Blockchain f...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Nemakonde, Pfano Nemangwele, Fhulufhelo Ratshitanga, Mukovhe University of Venda Faculty Science Engineering and Agriculture Dept. of Physics Thohoyandou South Africa Cape Peninsula University of Technology Dept. of Electrical Electronic and Computer Engineering Bellville South Africa

ISBN: (纸本)9798331535162

There is an increasing demand for affordable, decentralised and distributed electricity, which is one of the key motivating factors that has incentivised this review article's writing. Industrial 4.0 has been a conduit through which the conventional grids shift towards a self-autonomous system, and blockchain platforms are emerging as viable solutions to facilitate that paradigm. With the advent of peer-to-peer (P2P) energy trading, community-based energy exchanges have significantly contributed to reduced grid dependency. Machine learning (ML) is being implemented in predictive analytics for energy forecasting (EF), thereby aiding both distribution network operators (DNOs) and aggregators to precisely and predictably monitor, optimise and stabilise grid operations through smart agents. The main aim of this review paper is to provide a detailed analysis of the existing research and implementation approaches on blockchain-based P2P energy trading and a comparative overview of how ML applications aid predictive analytics in community-based energy exchanges. © 2025 IEEE.

关键词： Predictive analytics

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Blockchain Technology: Reshaping Telemedicine and Telehealth Services for Emergency Management

Blockchain Technology: Reshaping Telemedicine and Telehealth...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Singh, Neelam Mittal, Varsha Rawat, Vandana Kumar, Priyansh Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Uttrakhand Dehradun India Graphic Era Deemed to Be University Dept. of Computer Applications Uttrakhand Dehradun India

ISBN: (纸本)9798331508685

Both consumers and healthcare providers have benefited greatly from the quick development of software-based technology. The dominance of blockchains is impending as the medical care sector moves quickly towards digitization and customization. The development of blockchain provided answers to several problems that had long plagued the healthcare sector. However, not only in healthcare due to its capabilities, blockchain technology has found great success in a variety of industrial fields. The important factors propelling the growth of the healthcare blockchain market are the rise in data breaches, privacy concerns, fake pharmaceuticals, and provided answers to numerous problems that had long plagued the healthcare sector. Although telemedicine had been developing for a while, the covid-19 epidemic gave it the necessary kick. In order to facilitate social isolation, the pandemic made it necessary to expand virtual healthcare and distant therapy. Over the past year, the telemedicine market has experienced rapid expansion. In the current study, the adaptation issues and prospective prospects for blockchain technology in the telehealth and telemedicine sectors are *** on a systematic review, we have determined the existing state of the use of blockchain technology in a number of healthcare-related areas, the ideal state that would be achieved, the research gap regarding the usage of blockchain technology in a number of healthcare-related application areas, as well as a number of potential research agendas for future studies. © 2025 IEEE.

关键词： Telemedicine

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Classifying white blood cells using combining different convolutional neural networks

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Multimedia Tools and Applications 2025年 1-24页

作者： Toptaş, Murat Toptaş, Buket Hanbay, Davut Software Eng. Dept. Engineering and Natural Science Faculty Bandırma Onyedi Eylül University Balıkesir Turkey Computer Eng. Dept. Engineering Faculty Inonu University Malatya44280 Turkey

White blood cells are warrior cells that protect the human body against external factors. Each of these warrior cells performs a distinct task, making every piece of information about them highly valuable in the medical field. In this article, a classification framework for the four known types of white blood cells is proposed. It is hoped that the classification of these types will contribute to the prediction of diseases such as AIDS, malaria, leukemia, and many others. In the proposed method, images of white blood cells from the Blood Cell Classification and Detection dataset were used as input to Convolutional Neural Networks. The feature vectors extracted using these Convolutional Neural Network architectures were combined into a single vector. A Minimum Redundancy Maximum Relevance algorithm was then employed to identify the most effective features within the feature vector. Experiments were conducted using these selected features, and the analysis of each experiment was reported in detail. The Support Vector Machines classifier achieved an accuracy of 98.63% in classifying white blood cell types by combining features from multiple deep learning architectures. The experimental results demonstrated that the features obtained from different layers of the Convolutional Neural Networks had varying impacts on the classification performance. © The Author(s) 2025.

关键词： Convolutional neural networks

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Stability Analysis and Design of a Lightweight Concrete Canoe 2

Stability Analysis and Design of a Lightweight Concrete Cano...

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2nd International Conference in civil engineering for a Sustainable Planet, ICCESP 2024

作者： Keng, Azan Noor Raveendra, Velkuru Lalith Kumar Madhu, Ragala Meera Elayaraja, Srikanth Adhitha, R. Sivanantham, Pradeep Dept. of Civil Engineering SRM Institute of Science and Technology Chennai India

ISBN: (数字)9780784486047

ISBN: (纸本)9780784486047

This project focused on the development of a lightweight canoe made of carbon fiber, looking into its stability, design, and production. The objective was to make a breakthrough in concrete technology and structural engineering through the production of this lightweight concrete canoe. All this was achieved through an increase in sustainability and adding beneficial attributes to concrete produced using Class-C fly ash, P250 superplasticizer, rice husk ash, micro-silica, microspheres, and cenosphere. The comprehensive design mixture would be developed and cast in cubes for testing for compressive strength, accompanied by a variety of tests on lightweight prisms, panels, and cylinders. There are seven different mortar mixtures that have been developed for different ingredient combinations. Hull size for the canoe was finalized to be 6 m in length, 0.54 m wide, and 0.3 m high, keeping in mind parameters such as passenger capacity and the pattern of sitting arrangement. The study also utilizes Maxsurf software to evaluate the hull design for better maneuverability, speed, stability, and tracking. Stability analysis indicated that the canoe supported up to 48.2° of heel angle when it had a payload capacity of 150 kg and 52.6° of heel angle when the canoe was empty. This difference is caused by the uneven distribution of the weight as more passengers are loaded, thereby sinking the canoe deeper and decreasing its stability. This paper evaluates the potential of applying cenosphere in Portland cement for newer construction techniques that are more sustainable and resource efficient. © ASCE.

关键词： Carbon fibers

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Integrated and Intelligent Elder-Friendly E-commerce Framework using NLP Techniques

Integrated and Intelligent Elder-Friendly E-commerce Framewo...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Sivasanjith, G. Mohan Prabhu, N. Hemaswathi, S. Rajkumar, P. Rajeshkannan, C. Dhivya, R. Kare Dept. of Information Technology Krishnankovil India Artificial Intelligence and Data Science Indra Ganesan College of Engineering Tiruchirappalli India Mount Zion College of Engg. and Tech. Dept. of Computer Science and Engg. Pudukottai India Suguna College of Engineering Dept. of Computer Science and Engg. Coimbatore India M.Kumarasamy College of Engineering Dept. of Information Technology Karur India

ISBN: (纸本)9798331529833

All over the world, the population of elderly people is increasing a lot. There needs to be special attention to the welfare of the elderly person to make them confident in their independent quality of living. In recent years, researchers have shown much interest in smart home automation for people who live alone without any human assistance. However, the purchase of medical drugs, diapers, etc., outside the house has to be fulfilled by their family members or caretakers. In most cases, it is not feasible, as the family members may stay long away from the elders. Purchasing online is easy;however, analyzing the best product requires knowledge in each e-commerce application for better quality at a low cost. This paper proposes an integrated and intelligent e-commerce framework to avoid the drawback of purchasing items online in a single window using the NLP technique. The proposed framework provides offers, discounts, star ratings, and product reviews by analyzing other customers' review data and purchase patterns. It provides interactive data analysis at a single window for the ease of elderly persons. The performance of the proposed framework was evaluated by 120 elderly persons using a simple questionnaire with a statistical analysis of a sample purchase. © 2025 IEEE.

关键词： Recommender systems

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