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Review on Meta-heuristic Algorithm-Based Priority-Aware Computation Offloading in Edge Computing System

Journal of The Institution of Engineers (India): Series B 2025年 1-26页

作者： Pilli, Neelima Mohapatra, Debasis Reddy, Shiva Shankar Department of Computer Science and Engineering Biju Patnaik University of Technology Odisha Rourkela India Department of Computer Science and Engineering Parala Maharaja Engineering College Odisha Brahmpur India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College Andhra Pradesh Bhimavaram India

Advancements in smart applications highlight the need for increased processing and storage capacity at Smart Devices (SDs). To tackle this, Edge computing (EC) is enabled to offload SD workloads to distant edge servers. So, a variety of offloading strategies are offered in previous research to enhance the efficiency of SDs by offloading their workloads to neighboring cloudlets or distant cloud computing resources. Consequently, optimization techniques are crucial for scheduling computation offloading tasks in (ED) networks. To the greatest extent of current understanding, an extensive analysis of meta-heuristic-based computation offloading (CO) techniques in the EC has not been systematically surveyed. This systematic survey creates an inventory of CO in EC and further summarizes the most advanced recently available EC technologies. In this survey, three types of optimization techniques are examined: Lyapunov, Meta-heuristic, and Convex. The objective functions, application domains, offloading methods, evaluation techniques, merits, and demerits of the existing algorithms are summarized. The proposed survey undergoes four main CO processes: task scheduling, priority-aware task scheduling, load balancing, and edge server selection. In addition, this research provides a comparative analysis of the offloading techniques. Subsequently, unresolved problems and upcoming unfocused or inadequately covered research tasks are explored. © The Institution of Engineers (India) 2025.

关键词： Computation offloading

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ODD: Omni Differential Drive for Simultaneous Reconfiguration and Omnidirectional Mobility of Wheeled Robots

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IEEE Robotics and Automation Letters 2025年第6期10卷 5975-5982页

作者： Zhao, Ziqi Xie, Peijia Wang, Jiankun Meng, Max Q.-H Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen China Southern University of Science and Technology Jiaxing Research Institute Jiaxing China

Wheeled robots are highly efficient in human living environments. However, conventional wheeled designs, limited by degrees of freedom, struggle to meet varying footprint needs and achieve omnidirectional mobility. This paper proposes a novel robot drive model inspired by human movements, termed as the Omni Differential Drive (ODD). The ODD model innovatively utilizes a lateral differential drive to adjust wheel spacing without adding additional actuators to the existing omnidirectional drive. This approach enables wheeled robots to achieve both simultaneous reconfiguration and omnidirectional mobility. Additionally, a prototype was developed to validate the ODD, followed by kinematic analysis. Control systems for self-balancing and motion were designed and implemented. Experimental validations confirmed the feasibility of the ODD mechanism and the effectiveness of the control strategies. The results underline the potential of this innovative drive system to enhance the mobility and adaptability of robotic platforms. © 2016 IEEE.

关键词： Mobile robots

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Grape Guard:A YOLO-based mobile application for detecting grape leaf diseases

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Journal of Electronic science and technology 2025年第1期23卷 60-75页

作者： Sajib Bin Mamun Israt Jahan Payel Md.Taimur Ahad Anthony S.Atkins Bo Song Yan Li Department of Computer Science and Engineering Daffodil International UniversityDhaka1207Bangladesh Department of Computer Science University of Southern QueenslandToowoomba4350Australia Faculty of Digital TechnologyInnovationand BusinessStaffordshire UniversityStoke-on-TrentST42DEUnited Kingdom School of Engineering University of Southern QueenslandToowoomba4350Australia School of Mathematics Physics and ComputingUniversity of Southern QueenslandToowoomba4350Australia

Grape crops are a great source of income for *** yield and quality of grapes can be improved by preventing and treating *** farmer’s yield will be dramatically impacted if diseases are found on grape *** detection can reduce the chances of leaf diseases affecting other healthy *** studies have been conducted to detect grape leaf diseases,but most fail to engage with end users and integrate the model with real-time mobile *** study developed a mobile-based grape leaf disease detection(GLDD)application to identify infected leaves,Grape Guard,based on a TensorFlow Lite(TFLite)model generated from the You Only Look Once(YOLO)v8 model.A public grape leaf disease dataset containing four classes was used to train the *** results of this study were relied on the YOLO architecture,specifically YOLOv5 and *** extensive experiments with different image sizes,YOLOv8 performed better than ***8 achieved 99.9%precision,100%recall,99.5%mean average precision(mAP),and 88%mAP50-95 for all classes to detect grape leaf *** Grape Guard android mobile application can accurately detect the grape leaf disease by capturing images from grape vines.

关键词： Bacterial diseases Grape Guard Mobile-based application YOLOv5 YOLOv8

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Explainable cost-sensitive deep neural networks for brain tumor detection from brain MRI images considering data imbalance

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Multimedia Tools and Applications 2025年 1-28页

作者： Shawon, Md Tanvir Rouf Shibli, G. M. Shahariar Ahmed, Farzad Joy, Sajib Kumar Saha Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

This paper presents a research study on the use of Convolutional Neural Network (CNN), ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile models to efficiently detect brain tumors in order to reduce the time required for manual review of the report and create an automated system for classifying brain tumors. An automated pipeline is proposed, which encompasses five models: CNN, ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile. The performance of the proposed architecture is evaluated on a balanced dataset and found to yield an accuracy of 99.33% for fine-tuned InceptionV3 model. Furthermore, Explainable AI approaches are incorporated to visualize the model’s latent behavior in order to understand its black box behavior. To further optimize the training process, a cost-sensitive neural network approach has been proposed in order to work with imbalanced datasets which has achieved almost 4% more accuracy than the conventional models used in our experiments. The cost-sensitive InceptionV3 (CS-InceptionV3) and CNN (CS-CNN) show a promising accuracy of 92.31% and a recall value of 1.00 respectively on an imbalanced dataset. The proposed models have shown great potential in improving tumor detection accuracy and must be further developed for application in practical solutions. We have provided the datasets and made our implementations publicly available at -https://***/shahariar-shibli/Explainable-Cost-Sensitive-DeepNeural-Networks-for-Brain-Tumor-Detection-from-Brain-MRI-Images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Brain tumor CNN Cost-sensitive neural network EfficientNetB0 Explainable AI Grad-CAM InceptionV3 LIME NASNetMobile ResNet50 Score-CAM

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A moment-based pooling approach in convolutional neural networks for breast cancer histopathology image classification

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Neural Computing and Applications 2025年第2期37卷 1127-1156页

作者： Kumar, Arvind Singh, Chandan Sachan, Manoj Kumar Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Longowal Punjab Sangrur148106 India Department of Computer Science Punjabi University Punjab Patiala147002 India

Automated analysis of breast cancer (BC) histopathology images is a challenging task due to the high resolution, multiple magnifications, color variations, the presence of image artifacts, and morphological variability in the tissue images, often resulting in misdiagnosis. We propose four CNN models using a novel Zernike moments (ZMs)-based pooling approach, which replaces the existing global average pooling for the effective classification, referred to as CZP-CNN, MZP-CNN, SPMZP-CNN, and SPCZP-CNN. We utilize the ZMs-based pooling at three intermediate and last convolution layers. Of the four models, the two models, SPMZP-CNN and SPCZP-CNN, employ spatial pyramidal structures of feature maps to represent global and local characteristics of the feature maps. They effectively integrate the ZMs of various orders derived from the pyramidal representation of a feature map, representing the global and local aspects of shape and fine morphological details. The ability of ZMs is fully utilized to capture shape and morphological information available in the feature map using only a few low-order moments, which adds to the robustness of the proposed approach. Detailed experimental results on the publicly available BreaKHis dataset with four magnification levels and on the high-resolution BACH dataset demonstrate the proposed models’ effectiveness, robustness, and generalization ability. Among these proposed models, the complex ZMs-based SPCZP-CNN model achieves an impressive mean classification accuracy of 94.16% on the BreaKHis dataset and overall best binary and multiclass classification accuracy of 96.25% and 93.75% on the BACH dataset. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolution

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NC-Load: On-Demand Program Loading and Running for Computing Sharing Among IoT Devices

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IEEE Internet of Things Journal 2025年第9期12卷 12242-12256页

作者： Dong, Yanhao Duan, Sijing Lyu, Feng Zhao, Peng Zhang, Yongmin Ren, Ju Zhang, Yaoxue Central South University School of Computer Science and Engineering Hunan Changsha410083 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

The number of Internet of Things (IoT) devices has increased rapidly in recent years, but lack effective methods to integrate their computational power. In this article, we propose NC-Load, which couples IoT devices into a multiprocessor system, allowing process scheduling across different devices to share their computing power and improve overall throughput. Specifically, NC-Load consists of three key designs, i.e., remote page fault (RPF), lightweight program cropping, and identical memory layout migration, contributing to three merits compared to existing systems: 1) high storage efficiency: the target device launches the program with a locally stored lightweight icon and leverages RPFs to retrieve the required code/data from the source device;2) on-demand memory loading: only the required memory portions are transmitted when scheduling programs across different devices, which ensures quick recovery of the program;and 3) consistent memory layout: to ensure consistency of addresses after program offloading, the virtual memory area layout of the source device is migrated to the target device. We implement NC-Load on Linux 6.1 and conduct performance evaluation using unmodified programs and the N-Queens cases. The results demonstrate that NC-Load can achieve superior performance in terms of storage efficiency, program performance, memory usage, and throughput. © 2014 IEEE.

关键词： Multiprocessing systems

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HViTML: Hybrid vision transformer with machine learning-based classification model for glaucomatous eye

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Multimedia Tools and Applications 2025年 1-24页

作者： Singh, Piyush Bhushan Singh, Pawan Dev, Harsh Batra, Devanshu Chaurasia, Brijesh Kumar Department of Information Technology Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Babu Banarasi Das University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India

Glaucoma is currently one of the most significant causes of permanent blindness. Fundus imaging is the most popular glaucoma screening method because of the compromises it has to make in terms of portability, size, and cost. In recent years, convolution neural networks (CNNs) have revolutionized computer vision. Convolution is a "local" CNN technique that is only applicable to a small region surrounding an image. Vision Transformers (ViT) use self-attention, which is a "global" activity since it collects information from the entire image. As a result, the ViT can successfully gather distant semantic relevance from an image. This study examined several optimizers, including Adamax, SGD, RMSprop, Adadelta, Adafactor, Nadam, and Adagrad. With 1750 Healthy and Glaucoma images in the IEEE fundus image dataset and 4800 healthy and glaucoma images in the LAG fundus image dataset, we trained and tested the ViT model on these datasets. Additionally, the datasets underwent image scaling, auto-rotation, and auto-contrast adjustment via adaptive equalization during preprocessing. The results demonstrated that preparing the provided dataset with various optimizers improved accuracy and other performance metrics. Additionally, according to the results, the Nadam Optimizer improved accuracy in the adaptive equalized preprocessing of the IEEE dataset by up to 97.8% and in the adaptive equalized preprocessing of the LAG dataset by up to 92%, both of which were followed by auto rotation and image resizing processes. In addition to integrating our vision transformer model with the shift tokenization model, we also combined ViT with a hybrid model that consisted of six different models, including SVM, Gaussian NB, Bernoulli NB, Decision Tree, KNN, and Random Forest, based on which optimizer was the most successful for each dataset. Empirical results show that the SVM Model worked well and improved accuracy by up to 93% with precision of up to 94% in the adaptive equalization preprocess

关键词： Decision trees

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Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

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Neural Computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Nonlinear Optical Information Encoding with Grayscale Lithography Enabled Metasurfaces

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Nano Letters 2025年第18期25卷 7450-7456页

作者： Deng, Junhong Hu, Zixian Chen, Yu Chen, Jiafei Wang, Heng Li, Kingfai Kivshar, Yuri Li, Guixin Department of Materials Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Science Harbin Institute of Technology Shenzhen518055 China Nonlinear Physics Center Australian National University CanberraACT2601 Australia Institute for Applied Optics and Precision Engineering Southern University of Science and Technology Shenzhen518055 China

Optical information encoding is promising for many applications in sensing, data storage, and computing. Recently, various strategies have been suggested to encode optical information in planar devices. Among these, optical metasurfaces represent a flexible platform for manipulating multiple degrees of freedom of light with subwavelength scale meta-atoms. However, to realize both amplitude and phase control of light with metasurfaces, usually multiple meta-atoms per unit cell are required, so information density will be greatly reduced. Here, we develop a novel approach of nonlinear optical information encoding with grayscale lithography enabled hybrid metasurfaces composed of gold plasmonic meta-atoms deposited on an epsilon-near-zero material. By controlling the spacer layer thickness with electron beam grayscale lithography and varying orientation angles of the meta-atoms, we can control at the single-pixel level both the amplitude and phase of the generated second-harmonic waves. The proposed method opens new avenues for developing advanced nonlinear nanophotonic sources. © 2025 American Chemical Society.

关键词： Encoding (symbols)

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A two-stage method with twin autoencoders for the degradation trajectories prediction of lithium-ion batteries

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Journal of Energy Chemistry 2025年第4期103卷 759-772页

作者： Lei Cai Jing Yan Haiyan Jin Jinhao Meng Jichang Peng Bin Wang Wei Liang Remus Teodorescu School of Computer Science and Engineering Xi'an University of Technology School of Electrical Engineering Xi'an Jiaotong University Smart Grid Research Institute Nanjing Institute of Technology Department of Energy Aalborg University

To predict the lithium-ion(Li-ion) battery degradation trajectory in the early phase,arranging the maintenance of battery energy storage systems is of great ***,under different operation conditions,Li-ion batteries present distinct degradation patterns,and it is challenging to capture negligible capacity fade in early *** the data-driven method showing promising performance,insufficient data is still a big issue since the ageing experiments on the batteries are too slow and *** this study,we proposed twin autoencoders integrated into a two-stage method to predict the early cycles' degradation *** two-stage method can properly predict the degradation from course to *** twin autoencoders serve as a feature extractor and a synthetic data generator,***,a learning procedure based on the long-short term memory(LSTM) network is designed to hybridize the learning process between the real and synthetic *** performance of the proposed method is verified on three datasets,and the experimental results show that the proposed method can achieve accurate predictions compared to its competitors.

关键词： Autoencoder Battery degradation trajectory Early prediction Synthetic data generation

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