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Minimizing energy consumption in fixed node networks using a novel neutrosophic model: comparative analysis with standard and existing algorithms

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International Journal of Information Technology (Singapore) 2025年 1-15页

作者： Ghimire, Sangam Bhurtel, Nirjal Jha, Sudan Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Department of Computer Science and Engineering Kathmandu University 28 kilo Bagmati Dhulikhel 45210 Nepal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-kharj 11942 Saudi Arabia School of Computer Science and Engineering Lovely Professional University Punjab Phagwara 144411 India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Lebanon

The growing development and utilization of networked systems has led to more concern regarding the energy efficiency of these systems. In this paper, we present a novel approach to minimizing energy consumption in fixed node network for which we propose a new Neutrosophic model. This model leverages the unique strengths of Neutrosophic logic to enhance the efficiency by minimizing the energy consumption. The simulation of results is performed using Matplotlib (Python) with implementation parameters such as number of nodes (n) and additive white Gaussian noise (AWGN) (η). Additionally, we show a comparative analysis between the proposed model and other prevalent approaches used in minimizing energy consumption in networked systems. The comparative results shows improvement in energy efficiency, showcasing the potential of the proposed model as an alternative for optimizing energy consumption in networked systems. This work highlights the effectiveness of the proposed model and opens new avenues for future advancements in energy-efficient networked system using Neutrosophic approach. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Comparative analysis Energy-consumption optimization Networked systems Neutrosophic model Sensitivity analysis

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Enhancing Performances of Deep Neural Networks with Ensemble Learning Methods for Complex Human Activity Recognition Using Wearable Sensors 10

Enhancing Performances of Deep Neural Networks with Ensemble...

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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

Sensor-based human activity recognition (HAR) is a widely explored research area focused on identifying human behaviors by analyzing sensor-generated data, particularly one-dimensional time series signals. Such HAR systems have many real-world applications, including elderly health monitoring and athlete performance evaluation. This paper presents Ens-DeepNet, a novel ensemble learning approach designed for recognizing complex human activities using data from wearable sensors. While deep neural networks have proven effective for HAR tasks, their performance can be further improved through ensemble strategies. To this end, we propose an ensemble architecture that combines five distinct deep learning models - convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU). These models are integrated using a majority voting scheme to boost classification accuracy. The proposed method is evaluated using the UTwente dataset, which includes sensor data collected from ten individuals via tri-axial accelerometers and gyroscopes. Experimental findings demonstrate that the ensemble model achieves an impressive accuracy of 99.28%, surpassing the performance of each standalone model, whose accuracies range from 92.77%to 97.34%. This performance gain is particularly notable in recognizing complex activities such as eating, drinking coffee, smoking, and conversing. © 2025 IEEE.

关键词： Deep neural networks

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Optimal Location of Renewable Energy Generators in Transmission and Distribution System of Deregulated Power Sector:A Review

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Energy engineering 2025年第3期122卷 823-859页

作者： Digambar Singh Najat Elgeberi Mohammad Aljaidi Ramesh Kumar Rabia Emhamed Al Mamlook Manish Kumar Singla Department Electrical Engineering Government Polytechnic CollegeBTE LucknowEtah207120India University of Nevada RenoNV 89557USA Department of Computer Science Zarqa UniversityZarqa13110Jordan Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering&TechnologyChitkara UniversityChandigarh160002India Jadara University Research Center Jadara UniversityIrbid00962Jordan Department of Industrial Engineering Al Zawiya UniversityAl-ZawiyaP.O.Box 16418Libya Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Biosciences Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India

The literature on multi-attribute optimization for renewable energy source(RES)placement in deregulated power markets is extensive and diverse in *** study focuses on the most relevant publications directly addressing the research problem at ***,while the body of work on optimal location and sizing of renewable energy generators(REGs)in balanced distribution systems is substantial,only the most pertinent sources are cited,aligning closely with the study’s objective function.A comprehensive literature review reveals several key research areas:RES integration,RES-related optimization techniques,strategic placement of wind and solar generation,and RES promotion in deregulated powermarkets,particularly within transmission ***,the optimal location and sizing of REGs in both balanced and unbalanced distribution systems have been extensively *** demonstrate significant potential for standalone applications in remote areas lacking conventional transmission and distribution *** presents a thorough review of current modeling and optimization approaches for RES-based distribution system location and ***,it examines the optimal positioning,sizing,and performance of hybrid and standalone renewable energy *** paper provides a comprehensive review of current modeling and optimization approaches for the location and sizing of Renewable Energy Sources(RESs)in distribution systems,focusing on both balanced and unbalanced networks.

关键词： Optimization of RESs distributed generation modeling and selection of RESs hybrid systems standalone systems optimal location

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Numerical Simulation of Blood Flow Dynamics in a Stenosed Artery Enhanced by Copper and Alumina Nanoparticles

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computer Modeling in engineering & Sciences 2025年第2期142卷 1839-1864页

作者： Haris Alam Zuberi Madan Lal Amol Singh Nurul Amira Zainal Ali J.Chamkha Department of Applied Mathematics M.J.P.Rohilkhand UniversityBareilly243006India Fakulti Teknologi dan Kejuruteraan Mekanikal Universiti Teknikal MalaysiaMelaka76100Malaysia Forecasting and Engineering Technology Analysis(FETA)Research Group Universiti Teknikal MalaysiaMelaka76100Malaysia Faculty of Engineering Kuwait College of Science and TechnologyDoha35004Kuwait

Nanotechnology holds immense importance in the biomedical field due to its ability to revolutionize healthcare on a molecular *** by the imperative of enhancing patient outcomes,a comprehensive numerical simulation study on the dynamics of blood flow in a stenosed artery,focusing on the effects of copper and alumina nanoparticles,is *** study employs a 2-dimensional Newtonian blood flow model infused with copper and alumina nanoparticles,considering the influence of a magnetic field,thermal radiation,and various flow *** governing differential equations are first non-dimensionalized to facilitate analysis and subsequently solved using the 4th order collocation method,bvp4c module in *** approach obtains velocity and temperature profiles,revealing the impact of relevant parameters crucial in the biomedical *** findings of this study underscore the significance of understanding blood flow dynamics in stenosed arteries and the potential benefits of utilizing copper and alumina nanoparticles in treatment *** incorporation of nanoparticles introduces novel avenues for enhancing therapeutic interventions,particularly in mitigating the effects of *** elucidation of velocity and temperature profiles provides valuable insights into the behavior of blood flow under different conditions,thereby informing the development of targeted biomedical *** arterial curvature flow parameter influences temperature profiles,with increased parameters promoting more efficient heat *** elevated values of Prandtl number and thermal radiation parameter showcase the diminished temperature profiles,indicating stronger dominance of momentum diffusion over thermal diffusion and radiative heat transfer *** analysis of the pertinent physical parameters reveals that the Prandtl number has the most significant impact on blood flow dynamics.A statistical analysis of the present results and exist

关键词： Blood flow simulation stenosis copper and alumina nanoparticles thermal radiation curvature parameter

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NUMERICAL ENERGY DISSIPATION FOR TIME-FRACTIONAL PHASE-FIELD EQUATIONS

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Journal of Computational mathematics 2025年第3期43卷 515-539页

作者： Chaoyu Quan Tao Tang Jiang Yang School of Science and Engineering The Chinese University of Hong KongShenzhen 518172China Division of Science and Technology BNU-HKBU United International CollegeZhuhai 519087China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International CollegeZhuhai 519087China Department of Mathematics SUSTech International Center for Mathematics&National Center for Applied Mathematics Shenzhen(NCAMS)Southern University of Science and TechnologyShenzhen 518055China

The numerical integration of phase-field equations is a delicate task which needs to recover at the discrete level intrinsic properties of the solution such as energy dissipation and maximum *** the theory of energy dissipation for classical phase field models is well established,the corresponding theory for time-fractional phase-field models is still *** this article,we study certain nonlocal-in-time energies using the first-order stabilized semi-implicit L1 *** particular,we will establish a discrete fractional energy law and a discrete weighted energy *** extension for a(2-α)-order L1 scalar auxiliary variable scheme will be ***,we demonstrate that the energy bound is preserved for the L1 schemes with nonuniform time *** numerical experiments are carried to verify our theoretical analysis.

关键词： Time-fractional phased-field equation Allen-Cahn equations Cahn-Hilliard equations Caputo fractional derivative Energy dissipation

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BMWP: the first Bengali math word problems dataset for operation prediction and solving

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Discover Artificial Intelligence 2025年第1期5卷 1-15页

作者： Mondal, Sanchita Khatua, Debnarayan Mandal, Sourav Prasad, Dilip K. Sekh, Arif Ahmed School of Medical Science and Technology IIT Kharagpur West Bengal Kharagpur721302 India Department of Mathematics and Statistics Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur512213 India School of Computer Science and Engineering XIM University Odisha Puri752050 India Department of Computer Science Uit the Arctic University of Norway Tromsø9017 Norway

Solving math word problems of varying complexities is one of the most challenging and exciting research questions in artificial intelligence (AI), particularly in natural language processing (NLP) and machine learning (ML). Foundational language models such as GPT must be evaluated for intelligence, and solving word problems is a key method for this assessment. These problems become especially difficult when presented in low-resource regional languages such as Bengali. Word problem solving integrates the cognitive domains of language processing, comprehension, and transformation into real-world solutions. During the past decade, advances in AI and machine learning have significantly progressed in addressing this complex issue. Although researchers worldwide have primarily utilized datasets in English and some in Chinese, there has been a lack of standard datasets for low-resource languages such as Bengali. In this pioneering study, we introduce the first Bengali Math Word Problem Benchmark Data Set (BMWP), comprising 8653 word problems. We detail the creation of this dataset and the benchmarking methods employed. Furthermore, we investigate operation prediction from Bengali word problems using state-of-the-art deep learning (DL) techniques. We implemented and compared various standard DL-based neural network architectures, achieving an accuracy of 92±2%. The data set and the code will be available at https://***/SanchitaMondal/BMWP. © The Author(s) 2025.

关键词： Recurrent neural networks

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Fashion Design Transformation using Text-Guided Diffusion Models 4

Fashion Design Transformation using Text-Guided Diffusion Mo...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Adityan, Arjun Kabeleswar Vaiapury, Karthikeyan Palanisamy, T. Tangavel, Senthil Kumar Amrita Vishwa Vidyapeetham Department of Mathematics Coimbatore India Iitm Research Park Tcs Research and Innovation Chennai India Amrita School of Physical Sciences Department of Mathematics Amrita Vishwa Vidyapeetham Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331523923

We provide a novel approach for altering existing fashion designs through the integration of text-guided diffusion models with perceptual loss optimization. Effective technology for design evolution and modification is crucial for the rapid design iteration and prototyping of the fashion industry. We propose a pipeline that combines a LoRA-fine-tuned Stable Diffusion model with a perceptual loss to guarantee structural and stylistic faithfulness. The initial part is training a dedicated text-to-image generation model using fashion-specific data, subsequently refining the outputs through a perceptual loss mechanism to maintain structural integrity and improve stylistic coherence. After training with the DeepFashion-MultiModal dataset and evaluating performance on a custom dataset of essential garments, our method demonstrates exceptional preservation of stylistic coherence and structural integrity. Our approach offers a viable alternative for the automated transformation of fashion design while enhancing creative workflows within the fashion sector. © 2025 IEEE.

关键词： Hosiery manufacture

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VESPACE: A verifiable blockchain-based data space solution to empower the data economy

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computer Communications 2025年 239卷

作者： Costagliola, Andrea Roberta Mazzocca, Carlo Bujari, Armir Montanari, Rebecca Bellavista, Paolo Department of Computer Science and Engineering University of Bologna Bologna Italy Department of Information and Electrical Engineering and Applied Mathematics University of Salerno Fisciano Italy

In the rapidly evolving data economy, the ability to securely and efficiently share data between organizations has become paramount, unlocking new opportunities for innovation and growth. In this context, different initiatives have worked on conceptual proposals and enabling technological building blocks, addressing design aspects of data spaces. However, the current landscape lacks practical implementations and integration of secure data-sharing primitives supporting a decentralized data ecosystem. To this end, we conduct an analysis of previous efforts and initiatives, identifying gaps. We then introduce VESPACE, a blockchain-based platform for data spaces that enables participants to selectively and securely share verifiable data with authorized users while maintaining control over their access. Our framework incorporates data sovereignty principles implemented through Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and blockchain technology, qualifying decentralized identity and access control as key features to establish user trust in decentralized data ecosystems. We present a prototype system implementation of VESPACE, evaluating the design choices, showcasing the feasibility of our proposal. © 2025 The Authors

关键词： Authentication

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Two-Stage Real-Time Carbon Emission Monitoring for Low-Carbon Power System Operation: A Graph Neural Network-Based Approach

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Protection and Control of Modern Power Systems 2025年第3期10卷 166-183页

作者： Zhu, Zixuan Wang, Ruoheng Bu, Siqi Guglielmi, Roberto The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong999077 Hong Kong Research Institute for Smart Energy Policy Research Centre for Innovation and Technology Shenzhen Research Institute The Hong Kong Polytechnic University Research Centre for Grid Modernisation International Centre of Urban Energy Nexus Centre for Advances in Reliability and Safety Department of Electrical and Electronic Engineering Hong Kong999077 Hong Kong University of Waterloo Department of Applied Mathematics WaterlooN2L 3G1 Canada

As carbon emissions reduction is becoming increasingly important for sustainable development and carbon neutrality targets, the concept of carbon emission market has been recently proposed in order to essentially manage carbon emission on the demand side by allowing electricity consumers to purchase or sell carbon emission quotas. Hence, real-time demand-side carbon emission monitoring (DCEM), indicating the amount of carbon emission that each electricity consumer should be responsible for, becomes a necessity for the operation of the carbon emission market. However, the real-time DCEM cannot be achieved by carbon emission flow (CEF) analysis due to the unavailability of real-time power demand data. In this connection, this paper proposes a two-stage real-time DCEM method based on the graph neural network (GNN). In the first stage, power system operating scenario data, including the power generation capacity and power demand data, are collected to calculate carbon emission patterns through CEF analysis. In the second stage, a data-driven GNN-based model is designed to learn from historical daily carbon emission patterns and then realize accurate real-time DCEM with real-time available generation-side measurements only. Case studies on the 118-bus power system operated with day-ahead planning considering carbon emission are performed to demonstrate the accuracy and effectiveness of the proposed method. © 2019 Power System Protection and Control Press.

关键词： Electric power transmission networks

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Agricultural consumer Internet of Things devices: Methods for optimizing data aggregation

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Alexandria engineering Journal 2025年 125卷 692-699页

作者： Mengash, Hanan Abdullah Mahgoub, Hany Alshuhail, Asma Darem, Abdulbasit A. Majdoubi, Jihen Yafoz, Ayman Alsini, Raed Alghushairy, Omar Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Information Systems College of Computer Sciences & Information Technology King Faisal University Saudi Arabia Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia Engineering and Technology Unit Applied College Majmaah University Al Majmaah11952 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah21589 Saudi Arabia

With the advent of state-of-the-art computer and digital technology, modern civilisation has been immensely facilitated and optimised. The Internet of Things (IoT) has grown in importance in recent years, allowing us to monitor our physical environments and broadening our horizons. The "practice, science, or art" of farming is defined as tending to land, growing crops with the use of different tools and techniques, and then selling the harvested food. If farmers optimise their operations with the help of a Wireless Sensor Network (WSN), they will be able to work much more efficiently and effectively. Data aggregation involves collecting information from multiple sensors. The data aggregation process is optimised by applying metaheuristic techniques. A Genetic Algorithm (GA) is a method for modelling evolution that uses mutation, crossover, and natural selection as its building blocks. The key benefit of the Artificial Bee Colony (ABC) approach is that it simultaneously considers local and global search, and it doesn't get trapped calculating its local minima. Naturalistic algorithms like ALO model their hunting behaviour after that of ant-lions and doodlebugs. It manages to find a happy medium between exploration and exploitation with just one operator. Experimental evidences show that the proposed metaheuristic technique, ABC-ALO, which combines the best elements of Artificial Bee Colony and Ant Lion Optimisation, is superior to existing metaheuristic approaches in terms of lifetime computation, or the number of alive nodes at different round counts. © 2025

关键词： Genetic algorithms

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