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An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

Intelligent Automation & Soft Computing 2023年第5期36卷 1571-1583页

作者： Reem Alkanhel Kalaiselvi Chinnathambi C.Thilagavathi Mohamed Abouhawwash Mona A.Al duailij Manal Abdullah Alohali Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electronics and Communication Engineering Kongu Engineering CollegePerundurai638060India Department of Information Technology M.Kumarasamy College of EngineeringKarur639113India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the *** energy-efﬁcient data gathering methods in large-scale Wireless Sensor Networks(WSN)is one of the most difﬁcult areas of *** every sensor node has aﬁnite amount of *** power is the most signiﬁcant source in the *** is a well-known technique for enhan-cing the power feature in *** the proposed method multi-Swarm optimiza-tion based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network’s lifespan and routing *** using distributed data transmission modiﬁcation,an adaptive hier-archical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire *** begin,a hierarchical cluster-ing-based routing protocol is presented in terms of balancing node energy *** Multi-Swarm optimization(MSO)based Genetic Algorithms are proposed to select an efﬁcient Cluster Head(CH).It also improves the network’s longevity and optimizes the *** a result of the study’sﬁndings,the pro-posed MSO-Genetic Algorithm with Hill climbing(GAHC)is effective,as it increases the number of clusters created,average energy expended,lifespan com-putation reduces average packet loss,and end-to-end delay.

关键词： Clustering energy consumption genetic algorithm multi swarm optimization adaptive hierarchical clustering routing cluster head

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Sentiment Analysis On Arabic Companies Reviews

Sentiment Analysis On Arabic Companies Reviews

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Computing and Informatics (ICCI), International Conference on

作者： Aya E. Fouda Karim Salah Ahmed Karim Ashraf Mohamed Mayer Mamdouh Noshy Youssef ElKattan Amany Ahmed Mhran Ibrahim Abdelbaky Khaled M. Fouad School of Computer Science Nile University Giza Egypt Artificial Intelligence Department Faculty of Computers and Artificial Intelligence Benha University Banha Egypt Information Systems Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt

This study introduces an innovative approach to sentiment analysis, specifically tailored for the Arabic language, a domain that poses unique challenges due to its complex morphology and diverse dialects. Utilizing a substantial dataset of over 108,000 reviews related to Arabic companies, our primary objective was to develop a robust and reliable sentiment scoring system that caters to the intricacies of the Arabic language, aimed at assisting businesses in understanding customer sentiments more *** methodology encompassed an extensive preprocessing phase, crucial for preparing the dataset for accurate analysis. This phase included converting emojis and emoticons into textual descriptions a contemporary and expressive facet of digital communication, removing Arabic diacritics to standardize text input, and normalizing the text for consistency. The preprocessing was carefully designed to address the specific challenges posed by the Arabic script and dialectal *** the core sentiment analysis, we employed two robust machine learning models: Logistic Regression and Naive Bayes. Each model was meticulously chosen and adapted for its proven effectiveness in text classification, especially in handling the nuances of sentiment analysis within the Arabic text. Our approach not only leverages the strengths of these models but also adapts them to accommodate the linguistic characteristics of Arabic. The results of our study provide significant contributions to the field of sentiment analysis in Arabic. By addressing the unique challenges of the language and adapting conventional machine learning techniques accordingly, our research offers valuable insights and tools for businesses and researchers focused on the Arab market. These insights are instrumental for companies seeking to understand and respond to customer sentiments in a linguistically diverse and culturally rich region.

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Industrial Scenario of Renewable Energy-Based Electromobility 3rd

Industrial Scenario of Renewable Energy-Based Electromobilit...

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3rd International Conference on Energy systems, Drives and Automations, ESDA 2020

作者： Szymanski, Jerzy Zurek-Mortka, Marta Faculty of Transport Electrical Engineering and Computer Science Kazimierz Pulaski University of Technology and Humanities Radom Poland Department of Control Systems Lukasiewicz Research Network Institute for Sustainable Technology Radom Poland

ISBN: (纸本)9789811672736

The article presents the concept of a hybrid network topology in the enterprises with the use of a solar power plant and energy storage as well as a drive frequency converter for charging of transportation battery of wheels vehicles and powering of electric caterpillar mining machines. Two important functionalities of the drive frequency converter as a unidirectional EV battery charger and for powering electric motors are presented. The obtained results of the simulation and experimental tests of the presented model confirm the possibility of controlling the DC current value of connected to drive frequency converter of the diode rectifier unit charging the EV battery. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

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GLOW-ENV: A Dual-Data IoE-Based Approach for Integrating Glucose and Environmental Data into a Diabetes Recommendation System

GLOW-ENV: A Dual-Data IoE-Based Approach for Integrating Glu...

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18th International Joint Conference on Biomedical engineering systems and Technologies, BIOSTEC 2025

作者： Gaitán-Guerrero, Juan F. Martínez-Cruz, Carmen López, José L. Mata-Mata, Francisco Pérez-Cordón, Luis G. Serrano, José-María Morcillo-Martínez, Juana M. Verdejo-Espinosa, Ángeles Cuevas-Martínez, Juan C. Viciana-Abad, Raquel Reche-López, Pedro J. Pérez-Lorenzo, José M. Díaz-Jiménez, David Espinilla, Macarena Department of Computer Science University of Jaén Jaén23071 Spain Department of Languages and Computer Systems University of Granada Granada18071 Spain Psycology Department Faculty of Social Work University of Jaén Jaén23071 Spain Electrical Engineering Department University of Jaén Jaén23071 Spain Telecommunication Engineering Department University of Jaén Jaén23071 Spain

This paper introduces GLOW-ENV, an intelligent Internet of Everything (IoE)-driven mobile application designed with the objective of integrating real-time glucose monitoring data and environmental metrics to enhance diabetes care and management. The proposed IoE ecosystem integrates a continuous glucose monitoring with a personalized Artificial Intelligence model designed to predict glycemic fluctuations in a near-future. Additionally, GLOW-ENV integrates a rule-based recommendation system to dynamically adapt its suggestions based on contextual glucose and environmental data. This framework advances personalized diabetes care, contributing to their progression and well-being offering valuable insights and improving decision-making. © 2025 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Electronic health record

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A Battery-Less UHF RFID Sensor for Soil Moisture Monitoring

IEEE Journal of Radio Frequency Identification

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IEEE Journal of Radio Frequency Identification 2025年 9卷 286-294页

作者： Alsultan, Mohammed A. Melia-Segui, Joan Parron-Granados, Josep Lopez-Soriano, Sergio Internet Interdisciplinary Institute (IN3) Universitat Oberta de Catalunya Spain Rambla del Poblenou154 Barcelona 08018 Spain Faculty of Computer Science Multimedia and Telecommunication of the Universitat Oberta de Catalunya Rambla del Poblenou 156 Barcelona 08018 Spain Universitat Autònoma de Barcelona Department of Telecommunication and Systems Engineering Bellaterra 08193 Spain

Soil moisture monitoring is essential for optimizing irrigation strategies, enhancing crop yields, and conserving water resources in precision agriculture. Traditional sensing methods often rely on battery-powered devices, which require maintenance and periodic replacement. This work introduces a batteryless ultrahigh frequency radio frequency identification (UHF RFID) soil moisture sensor that leverages RFID technology and an interdigitated capacitor (IDC) for capacitive sensing. The proposed sensor integrates a meandered dipole antenna and an EM4152 RFID chip, enabling wireless monitoring of soil Volumetric Water Content (VWC) without the need for an external power source. The sensor’s performance is validated through controlled soil moisture experiments, where capacitance readings are correlated with reference measurements from the commercial TEROS 10 soil moisture sensor. The sensor was tested and calibrated using three different soil types: sandy, clay, and a commercial combo substrate. The results demonstrate strong linear correlations with TEROS 10 measurements across all soil types, with coefficients of determination of R² = 0.9648 (sandy), R² = 0.9512 (clay), and R² = 0.9444 (combo). Furthermore, tests conducted at varying water contents and a read range of up to 3.5 meters validate the sensor’s robustness across different soil conditions. The findings highlight the potential of battery-less RFID-based sensing for sustainable and maintenance-free soil moisture monitoring in agricultural applications. © 2017 IEEE.

关键词： Battery-less capacitive sensing EM4152 RFID chip IDC interdigitated capacitor precision agriculture soil moisture monitoring sustainable agriculture ultra high frequency radio frequency identification (UHF-RFID) volumetric water content (VWC) wireless sensor

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An Effective Online Collaborative Training in Developing Listening Comprehension Skills

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computer systems science & engineering 2021年第8期38卷 131-140页

作者： Shakeel Ahmed Munazza Ambreen Muneer Ahmad Abdulellah A.Alaboudi Roobaea Alroobaea NZ Jhanjhi Department of Secondary Instructors Education Faculty of EducationAllama Iqbal Open UniversityIslamabadPakistan Department of Information Systems Faculty of Computer Science&ITUniversiti Malaya50603 Kuala LumpurMalaysia College of Computer Science Shaqra UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500Malaysia

The COVID-19 outbreak severely affected formal face-to-face classroom teaching and ***-based online education and training can be a useful measure during the *** the Pakistani educational context,the use of ICT-based online training is generally sporadic and often unavailable,especially for developing English-language instructors’listening comprehension *** major factors affecting availability include insufficient IT resources and infrastructure,a lack of proper online training for speech and listening,instructors with inadequate academic backgrounds,and an unfavorable environment for ICT-based training for listening *** study evaluated the effectiveness of ICT-based training for developing secondary-level English-language instructors’listening comprehension *** this end,collaborative online training was undertaken using random ***,60 private-school instructors in Chakwal District,Pakistan,were randomly selected to receive online-listening training sessions using English *** experimental group achieved significant scores in the posttest ***,there were substantial improvements in the participants’listening skills via online *** the unavailability of face-to-face learning during COVID-19,this study recommends using ICT-based online training to enhance listening comprehension *** policymakers should revise curricula based on online teaching methods and modules.

关键词： COVID-19 online training remote teaching computers in education listening comprehension English language

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ELMap: Area-Driven LUT Mapping with $k$-LUT Network Exact Synthesis

ELMap: Area-Driven LUT Mapping with $k$-LUT Network Exact Sy...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Hongyang Pan Keren Zhu Fan Yang Zhufei Chu Xuan Zeng State Key Lab of Integrated Circuits and Systems School of Microelectronics Fudan University Shanghai China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Mapping to $k$ -input lookup tables ( $k$ -LUTs) is a critical process in field-programmable gate array (FPGA) synthesis. However, the structure of the subject graph can introduce structural bias, which refers to the dependency of mapping results on the inherent graph structure, often leading to suboptimal results. To address this, we present ELMap, an area-driven LUT mapping framework. It incorporates structural choice during the collapsing phase. This enables dynamic decomposition, maximizing local-to-global optimization transfer. To ensure seamless integration between the optimization and mapping processes, ELMap leverages exact $k$ -LUT synthesis to generate area-optimal sub-LUT networks. Experiments on the EPFL benchmark suite demonstrate that ELMap significantly outperforms state-of-the-art methods. Specifically, in 6-LUT mapping, ELMap reduces the average LUT area by 8.5% and improves the area-depth-product (ADP) by 5.8%. In 4-LUT remapping, it reduces the average LUT area by 17.6% and improves the ADP by 2.4%.

关键词： Europe Benchmark testing Table lookup Logic Optimization Field programmable gate arrays

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ELMap: Area-Driven LUT Mapping with k-LUT Network Exact Synthesis

ELMap: Area-Driven LUT Mapping with k-LUT Network Exact Synt...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Pan, Hongyang Zhu, Keren Yang, Fan Chu, Zhufei Zeng, Xuan School of Microelectronics Fudan University State Key Lab of Integrated Circuits and Systems Shanghai China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9783982674100

Mapping to k-input lookup tables (k-LUTs) is a critical process in field-programmable gate array (FPGA) synthesis. However, the structure of the subject graph can introduce structural bias, which refers to the dependency of mapping results on the inherent graph structure, often leading to suboptimal results. To address this, we present ELMap, an area-driven LUT mapping framework. It incorporates structural choice during the collapsing phase. This enables dynamic decomposition, maximizing local-to-global optimization transfer. To ensure seamless integration between the optimization and mapping processes, ELMap leverages exact k-LUT synthesis to generate area-optimal sub-LUT networks. Experiments on the EPFL benchmark suite demonstrate that ELMap significantly outperforms state-of-the-art methods. Specifically, in 6-LUT mapping, ELMap reduces the average LUT area by 8.5% and improves the area-depth-product (ADP) by 5.8%. In 4-LUT remapping, it reduces the average LUT area by 17.6% and improves the ADP by 2.4%. © 2025 EDAA.

关键词： Table lookup

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A Code-Switched Arabic-English Sentiment Analysis Approach Based on Deep-Learning

A Code-Switched Arabic-English Sentiment Analysis Approach B...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Tasneem S. Almasah Gamal A. Ebrahim Marwa A. Abdelaal Electrical Engineering Department Faculty of Engineering The British University in Egypt Cairo Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Computer Science Department Faculty of Computers and Information Sadat Academy for Management Sciences Cairo Egypt

Sentiment analysis within Online Social Networks (OSNs) becomes a major challenge. Mainly, because of the large amount of data on social networks and the mix of different languages that can be used in these environments. Due to the lack of existing research and datasets on code-switched Arabic-English sentiment analysis, this study creates a new dataset collected from various online social networks. The data is subjected to several preprocessing steps before being analyzed using various deep learning architectures. Moreover, a thorough evaluation of the preprocessing techniques and the performance of the deep learning models have been conducted. This is achieved using comprehensive Twitter datasets in Arabic and English. The study reveals that individual deep learning architectures, specifically, Convolutional Neural Networks (CNNs), Bidirectional Long Short Term Memory (Bi-LSTM), and LSTM demonstrated superior results compared to their hybrid counterparts. Notably, CNN emerged as the most accurate model with an accuracy of 83%, followed closely by Bi-LSTM at 82.3%. Additionally, the paper presents the deployment of an automated machine learning tool, which is Auto-Keras. Furthermore, it designs an optimized sentiment analysis model that aims to enhance accuracy. This approach leads to an increase in the model accuracy to 86%.

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Streamflow Prediction in the Mekong River Basin Using Deep Neural Networks

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IEEE Access 2023年 11卷 97930-97943页

作者： Nguyen, Thi-Thu-Ha Vu, Duc-Quang Mai, Son T. Dang, Thanh Duc National Taiwan University Department of Bioenvironmental Systems Engineering Taipei10617 Taiwan National Central University Department of Computer Science and Information System Taoyuan320317 Taiwan Thai Nguyen University of Education Thai Nguyen250000 Viet Nam Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom Institute for Computational Science and Artificial Intelligence Van Lang University Laboratory of Environmental Sciences and Climate Change Ho Chi Minh City70000 Viet Nam Van Lang University Faculty of Environment Ho Chi Minh City70000 Viet Nam

In recent years, the Mekong River Basin (MRB), one of the largest river basins in Southeast Asia, has experienced severe impacts from extreme droughts and floods. Streamflow forecasting has become crucial for effective risk management strategies in the region. However, this task presents significant challenges due to rapid climate changes and the presence of numerous newly constructed upstream dams, which disrupt the natural flow. In this paper, we develop multiple deep learning models (incl. Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long short-term Memory (LSTM), and Transformer) to predict streamflow with different lead time forecasts based on observed meteorological variables and climatic indices (i.e., discharge, water level, precipitation, and temperature) from 1979 to 2019. The results indicate that LSTM obtains high performance for streamflow prediction in both dry and wet seasons while Transformer is not recommended for long-term prediction, especially in the dry season. The proposed deep learning models capture well the fluctuation of river flow in the MRB during the period of high-dam development, especially LSTM (NSE ≥ 0.8). The models' performances are enhanced with the adding of temperature for short-term prediction while precipitation was the most sensitive variable for long-term one. Such proposed models are essential for government agencies to plan mitigation and adaptation strategies at different periods, which can range from days to years. © 2013 IEEE.

关键词： Floods

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