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IoT with Evolutionary Algorithm Based Deep Learning for Smart Irrigation System

computers, Materials & Continua 2022年第4期71卷 1713-1728页

作者： P.Suresh R.H.Aswathy Sridevi Arumugam Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammad Alamgeer Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical Sciences Adithya Institute of TechnologyCoimbatore641107India Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia

In India, water wastage in agricultural fields becomes a challengingissue and it is needed to minimize the loss of water in the irrigation *** the conventional irrigation system needs massive quantity of waterutilization, a smart irrigation system can be designed with the help of recenttechnologies such as machine learning (ML) and the Internet of Things (IoT).With this motivation, this paper designs a novel IoT enabled deep learningenabled smart irrigation system (IoTDL-SIS) technique. The goal of theIoTDL-SIS technique focuses on the design of smart irrigation techniquesfor effectual water utilization with less human interventions. The proposedIoTDL-SIS technique involves distinct sensors namely soil moisture, temperature, air temperature, and humidity for data acquisition purposes. The sensordata are transmitted to the Arduino module which then transmits the sensordata to the cloud server for further process. The cloud server performs the dataanalysis process using three distinct processes namely regression, clustering,and binary classification. Firstly, deep support vector machine (DSVM) basedregression is employed was utilized for predicting the soil and environmentalparameters in advances such as atmospheric pressure, precipitation, solarradiation, and wind speed. Secondly, these estimated outcomes are fed intothe clustering technique to minimize the predicted error. Thirdly, ArtificialImmune Optimization Algorithm (AIOA) with deep belief network (DBN)model receives the clustering data with the estimated weather data as inputand performs classification process. A detailed experimental results analysisdemonstrated the promising performance of the presented technique over theother recent state of art techniques with the higher accuracy of 0.971.

关键词： Smart irrigation precision agriculture internet of things deep learning machine learning

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Subjective and Objective Quality Assessments of AI-Generated Images for Language E-Learning

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IEEE Access 2025年 13卷 100667-100682页

作者： Chibuike Onuoha Vu Hai Dang Nguyen Duc Nam Truong Thu Huong Pham Ngoc Nam Truong Cong Thang Department of Computer Science and Information Systems The University of Aizu Aizuwakamatsu Japan School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Vietnam College of Engineering and Computer Science VinUniversity Hanoi Vietnam

AI-generated images (AIGIs) are becoming popular and can be employed in many applications, owing to Generative AI (GAI). Researchers have developed models that can be used to generate images for different scenarios. In addition, researchers have proposed datasets of natural scene images for language learning and different AIGI-quality datasets for general applications. For e-learning, particularly in the context of language learning, no AIGI dataset is currently available. To fill this gap, we first proposed an AIGI quality dataset for language learning. Both subjective and objective assessments have been conducted on the proposed dataset. The findings from subjective assessment show that higher perceptual quality also corresponds to a more substantial alignment. It also shows that the average MOS scores of images generated from Stability AI models are similar and lower than images generated by the Dall.E3 model. The results of the objective assessment indicate that the performance of off-the-shelf quality models is generally low. In addition, results from finetuning learning-based quality models show that significant gains and improvements can be achieved using the dataset. The results of the alignment evaluation show that the HPS model is the best, and realistic images in the dataset produced the best alignment correlation compared to the other styles in the dataset. The findings also show that multimodal large language models, such as vision-enabled GPT-4 (GPT-4V), still struggle to produce alignment scores that correlate with humans.

关键词： Electronic learning Visualization Text to image Quality of experience Quality assessment Diffusion models Training data Training Generators Generative AI

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Variable-Length XP-HARQ for URLLC 16

Variable-Length XP-HARQ for URLLC

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Feng, Jiahui Shi, Zheng Fu, Yaru Wang, Hong Yang, Guanghua Ma, Shaodan The School of Intelligent Systems Science and Engineering Jinan University China The School of Science and Technology Hong Kong Metropolitan University Hong Kong Nanjing University of Posts and Telecommunications China University of Macau The Department of Electrical and Computer Engineering China

ISBN: (纸本)9798350390643

A variable-length cross-packet hybrid automatic repeat request (VL-XP-HARQ) is proposed to accommodate ultrareliable low-latency communications (URLLC). The spectral efficiency (SE) is firstly derived in terms of the outage probabilities, with which the SE is proved to be upper bounded by the ergodic capacity (EC). Moreover, to facilitate the maximization of the SE, the asymptotic outage probability is obtained at high signal-to-noise ratio (SNR), with which the SE is maximized by properly choosing the number of new information bits while guaranteeing outage requirement. By applying Dinkelbach's transform, the fractional objective function is transformed into a subtraction form, which can be decomposed into multiple sub-problems through alternating optimization. By noticing that the asymptotic outage probability is a convex function, each sub-problem can be easily relaxed to a convex problem by adopting successive convex approximation (SCA). Finally, numerical results are presented for verification. © 2024 IEEE.

关键词： Spectrum efficiency

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Autoencoder and Teaching-learning-based Optimizer for Mobile Edge Computing System Optimization Problems

Autoencoder and Teaching-learning-based Optimizer for Mobile...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Xu, Dian Zhou, Mengchu Yuan, Haitao Institute of Systems Engineering Macau University of Science and Technology Macao China New Jersey Institute of Technology Department of Electrical and Computer Engineering Newark United States School of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (纸本)9798350337020

By using an autoencoder as a dimension reduction tool, an Autoencoder-embedded Teaching-Learning Based Optimization (ATLBO) has been proved to be effective in solving high-dimensional computationally expensive problems through several widely used function problems. However, the following two crucial issues have not been resolved, 1) ATLBO should be verified by solving real-life optimization problems;and 2) how autoencoder parameters and structures impact AEO's performance. In this work, ATLBO is verified by an energy consumption minimization problem (ECM) in mobile edge computing systems. To design an effective autoencoder for ATLBO, this work proposes a parameter tuning optimization strategy for autoencoders. By using the proposed Autoencoder Parameter Tuning (APT) strategy, ATLBO can enjoy higher robustness than those without it. The experimental results show that it is three to six times better than state-of-the-art methods in solving ECM. We consider the strategy-induced overhead and take the execution time as the primary criterion to evaluate them. In addition, the experimental results show that, against the conventional wisdom that higher-accuracy auto encoders bring higher system performance, lower-accuracy ones can actually assist ATLBO in locating the best solutions. This work promotes a novel application of autoencoders in optimization theory and practice. © 2023 IEEE.

关键词： autoencoder Autoencoder-embedded teaching-learning based optimization (ATLBO) High-dimensional expensive problems (HEPs) Mobile edge computing systems

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Linear Model-Based Optimal VVC Intercoding Rate Control Scheme 1

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Proceedings of the 4th International Conference on Neural Computing for Advanced Applications, NCAA 2023

作者： Wei, Xuekai Zhou, Mingliang Liao, Xingran State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering University of Macau 999078 China School of Computer Science Chongqing University Chongqing400044 China Department of Computer Science City University of Hong Kong Kowloon 999077 Hong Kong

ISBN: (数字)9789819958443

ISBN: (纸本)9789819958436

In this paper, we propose a rate control (RC) scheme with a linear model in versatile video coding (VVC) intercoding, our method introduces a compromise global Lagrange multiplier to minimize the distortion induced under group-of-pictures (GOP)- and frame-level bit budget constraints. To obtain the optimal solution, the corresponding problem is transformed into a convex optimization problem. Then, the Karush-Kuhn-Tucker (KKT) conditions are used to obtain the optimal quantization parameter (QP). The experimental results show that our RC algorithm achieves better coding efficiency, a better RC effect, and higher subjective quality than the default algorithm in VVC Test Model (VTM) 17.0 and other state-of-the-art algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convex optimization

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Towards a Longitudinal Comparison Between Different Strategies for Android Malware Detection 3

Towards a Longitudinal Comparison Between Different Strategi...

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3rd International Conference on Theoretical and Applicative Aspects of computer science, ICTAACS 2023

作者： Mesbah, Abdelhak Baddari, Ibtihel Riahla, Mohamed Amine University m'Hamed Bougara of Boumerdes Faculty of Sciences Limose Laboratory Department of Computer Science Boumerdes Algeria University m'Hamed Bougara of Boumerdes Faculty of Technology Limose Laboratory Department of Electrical Systems Engineering Boumerdes Algeria

ISBN: (纸本)9798350385854

The growing popularity of the Android platform makes it a target of malware authors. The effective identification of such malware is an ongoing challenge. Several methods using machine learning have been proposed to prevent this threat. These methods are usually conventionally evaluated without considering the extent of performance over time. Given the evolving nature of both malware and benign apps, conventional evaluation may lack information. To imitate reality, this study compares the longitudinal performance of different machine learning models, using different strategies that combine permissions and API calls as features extracted through static analysis. Thus, to determine which strategy of features on which classifier are most effective to characterize malware for building a robust malware detector. To achieve this goal, on the one hand, we use a large real-world app set consisting of 100K (50k benign, 50k malware) apps date-labeled, collected across ten years, first seen between 2013 and 2022. On the other hand, each feature's strategy is fed into five classifiers (i.e., SVM, RF, LR, DT, and ANN), using old apps for the training and new apps for the evaluation. Among the assessed machine learning models, the SVM achieves the most promising results over time by employing the combination strategy of the high difference usage of API calls and permissions. © 2023 IEEE.

关键词： Static analysis

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Facial Surface Anthropometric Features and Measurements With an Emphasis on Rhinoplasty (vol 42, pg 133, 2022)

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AESTHETIC SURGERY JOURNAL 2022年第2期42卷 230-230页

作者： Celikoyar, M. Mazhar Perez, Michael F. Akbas, M. Ilhan Topsakal, Oguzhan Department of Otolaryngology Istanbul Florence Nightingale Hospital Istanbul Turkey Computer Science Department Florida Polytechnic University Lakeland FL USA Electrical Computer Software and Systems Engineering Department Embry-Riddle Aeronautical University Daytona Beach FL USA

Background Facial features and measurements are utilized to analyze patients’ faces for various reasons, including surgical planning, scientific communications, patient-surgeon communications, and post-surgery evaluations. Objectives There are numerous descriptions regarding these features and measurements scattered throughout the literature, and the authors did not encounter a current compilation of these parameters in the medical literature. Methods A narrative literature review of the published medical literature for facial measurements used for facial analysis in rhinoplasty was conducted through the electronic databases MEDLINE/PubMed and Google Scholar, along with a citation search. Results A total of 61 facial features were identified: 45 points (25 bilateral, 20 unilateral), 5 lines (3 bilateral, 2 unilateral), 8 planes, and 3 areas. A total of 122 measurements were identified: 48 distances (6 bilateral, 42 unilateral), 57 angles (13 bilateral, 44 unilateral), and 17 ratios. Supplemental figures were created to depict all features and measurements utilizing a frontal, lateral, or basal view of the face. Conclusions This paper provides the most comprehensive and current compilation of facial measurements to date. The authors believe this compilation will guide further developments (methodologies and software tools) for analyzing nasal structures and assessing the objective outcomes of facial surgeries, in particular rhinoplasty. Moreover, it will improve communication as a reference for facial measurements of facial surface anthropometry, in particular rhinoplasty.

关键词： anthropometry face rhinoplasty software surgical procedures operative nose facial features

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h5bench: A unified benchmark suite for evaluating HDF5 I/O performance on pre-exascale platforms

h5bench: A unified benchmark suite for evaluating HDF5 I/O p...

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作者： Bez, Jean Luca Tang, Houjun Breitenfeld, Scot Zheng, Huihuo Liao, Wei-Keng Hou, Kaiyuan Huang, Zanhua Byna, Suren Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA United States HDF Group ChampaignIL United States Data Science Group Argonne National Laboratory LemontIL United States Electrical and Computer Engineering Department Northwestern University EvanstonIL United States Computer Science Department Northwestern University EvanstonIL United States Department of Computer Science and Engineering The Ohio State University OH United States

Parallel I/O is a critical technique for moving data between compute and storage subsystems of supercomputers. With massive amounts of data produced or consumed by compute nodes, high-performant parallel I/O is essential. I/O benchmarks play an important role in this process;however, there is a scarcity of I/O benchmarks representative of current workloads on HPC systems. Toward creating representative I/O kernels from real-world applications, we have created h5bench, a set of I/O kernels that exercise hierarchical data format version 5 (HDF5) I/O on parallel file systems in numerous dimensions. Our focus on HDF5 is due to the parallel I/O library's heavy usage in various scientific applications running on supercomputing systems. The various tests benchmarked in the h5bench suite include I/O operations (read and write), data locality (arrays of basic data types and arrays of structures), array dimensionality (one-dimensional arrays, two-dimensional meshes, three-dimensional cubes), I/O modes (synchronous and asynchronous). In this paper, we present the observed performance of h5bench executed along several of these dimensions on existing supercomputers (Cori and Summit) and pre-exascale platforms (Perlmutter, Theta, and Polaris). h5bench measurements can be used to identify performance bottlenecks and their root causes and evaluate I/O optimizations. As the I/O patterns of h5bench are diverse and capture the I/O behaviors of various HPC applications, this study will be helpful to the broader supercomputing and I/O community. © 2024 Lawrence Berkeley National laboratory, The HDF Group, UChicago Argonne, LLC and The Authors. Concurrency and Computation: Practice and Experience published by John Wiley & Sons Ltd.

关键词： Supercomputers

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Analyzing the Impact of Security Measures on Wi-Fi Quality of Service 2

Analyzing the Impact of Security Measures on Wi-Fi Quality o...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Kopcho, Thomas J. Fouda, Mostafa M. Krome, Cameron J. Quach, Anna T. Olaveson, Israel G. Idaho State University Department of Computer Science PocatelloID United States Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Ai Data & Visualization Idaho National Laboratory Idaho FallsID United States

ISBN: (纸本)9798331529529

This study examines the relationship between Quality of Service (QoS) and security in Wi-Fi networks. Using a detailed dataset consisting of QoS metrics of file transfers under various security configurations, we analyze the impact of these settings on network performance. Our findings provide a quantification to accurately show that different encryption protocols, such as TKIP and CCMP, significantly affect QoS, with disabling security generally improving performance. The study provides a framework for optimizing security settings to enhance both security and QoS in future wireless networks. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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A Lightweight AI Model for Anomaly Detection in Wireless Networks 2

A Lightweight AI Model for Anomaly Detection in Wireless Net...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Kopcho, Thomas J. Fouda, Mostafa M. Krome, Cameron J. Idaho State University Department of Computer Science PocatelloID United States Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Idaho National Laboratory Ai Data & Visualization Idaho FallsID United States

ISBN: (纸本)9798331529529

Detecting network anomalies is critical for wireless network security and reliability. Traditional AI methods often require substantial computational resources, particularly when deployed on cloud servers, leading to increased network load and latency. In contrast, deploying AI models directly on end devices enables real-time data processing at the source, facilitating faster anomaly detection and mitigation. We propose a lightweight hierarchical AI model where initial inferences are made by a simple model and outputs confidence scores. Using a modified TOPSIS-based method, we determine an optimal confidence score threshold. Inferences below this threshold are forwarded to a more complex model for accurate analysis, reducing overall computational demands while maintaining high detection performance. Our approach is tested on a 5 G network dataset, demonstrating competitive performance compared to other anomaly detection models. © 2024 IEEE.

关键词： 5G mobile communication systems

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