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Hybrid CNN Model for Classification of Rumex Obtusifolius in Grassland

IEEE ACCESS 2022年 10卷 90940-90957页

作者： Al-Badri, Ahmed Husham Ismail, Nor Azman Al-Dulaimi, Khamael Rehman, Amjad Abunadi, Ibrahim Bahaj, Saeed Ali Univ Teknol Malaysia Fac Engn Sch Comp Johor Baharu 81310 Malaysia Al Nahrain Univ Coll Sci Comp Sci Dept Baghdad 10072 Iraq Queensland Univ Technol Sch Elect Engn & Robot Brisbane Qld 4059 Australia Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh 11586 Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Business Adm MIS Dept Al Kharj 11942 Saudi Arabia Hadhramout Univ Coll Engn & Petr Dept Comp Engn Mukalla 50511 Yemen

Rumex obtusifolius Linnaeus (R. obtu. L.) is one of the vital broad-leaved weeds in grassland that needs removal. It affects dairy products and reduces their quality. Hand-removal methods are costly and time-consuming. Chemical treatment using herbicides has a negative impact on crops and causes environmental pollution. In smart farming, weeding is performed by using computer vision to recognize the weeds efficiently and effectively. Conventional machine learning (ML)-based algorithms face challenges, especially in identifying the weeds in real-world data due to a lack of features. Deep learning (DL) approaches use self-learning to extract all potential features that assist in classifying malignant weed species accurately. Recently, single deep learning methods achieved high performance in identifying well-separated and illumination but suffered from misclassification in more sophisticated cases such as overlapping and partial occlusion leaves. This paper presents a hybrid Convolutional Neural Network (CNN) model of three state-of-the-art CNNs to classify Rumex obtusifolius. The proposed model utilizes convolutional neural networks to extract features and classify images. The framework of the proposed method comprises three paramount stages to accomplish the classification key idea, including the data preparation phase, pre-processing phase, and classification phase. A hybrid model of three CNN extractor networks is used as the backbone in the classification stage. Our tested data is real-world data that includes multi-circumstances (overlap, occlusion, various illuminations, etc.) acquired from nature. The first extractor is the Visual Graphics Group-16 (VGG-16) for well-separated leaves and non-complicated issues. The second extractor is Residential Energy Services Network-50 (ResNet-50), to overcome complex real-world issues. The third extractor is Inception-v3 to solve the illumination problem. Therefore, combining three networks into one model improves the disc

关键词： Feature extraction Convolutional neural networks Lighting Computational modeling Crops Chemicals Visualization Economics Agriculture Classification algorithms CNN networks ensemble models real-world data weed classification economic growth

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Hybrid Segmentation Method With Confidence Region Detection for Tumor Identification

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IEEE ACCESS 2021年 9卷 35256-35278页

作者： Ejaz, Khurram Rahim, Mohd Shafry Mohd Bajwa, Usama Ijaz Chaudhry, Huma Rehman, Amjad Ejaz, Farhan Univ Teknol Malaysia Sch Comp Johor Baharu 81310 Malaysia COMSATS Univ Dept Informat Technol Lahore Campus Islamabad Lahore 54000 Pakistan Victoria Univ Coll Engn & Sci Footscray Vic 3011 Australia Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab CCIS Riyadh 11586 Saudi Arabia Nokia Dept RF Planning Al Khobar 31952 Saudi Arabia

Segmentation methods can mutually exclude the location of the tumor. However, the challenge of complex location or incomplete identification is located in segmentation challenge dataset. Identificationof tumor location is difficult due to the variation of intensities in MRI image. Vairation of intensity extends up to edema. Confidence Region with Contour Detection identifies the variation of intensities and level set algorithm (Region Scale Fitting) is used to delineate among the region of inner and outer of the tumor. Automatic feature selection method is required due to data complexity. An improved Self Organization Feature Map. Method is required. Weighted SOM Map selects a deterministic feature. This feature is one higher trained accuracy feature. When this specific feature is combines with cluster therefore it is known as deterministic feature clustering. This method gives confidence element. Confidence Region with Contour detection is facing the issue due to extended variations of intensities. These intensities are segmented by hybrid SOM Pixel labelling with Reduce Cluster Membership and Deterministic Feature Clustering. This hyhbrid method segments the complex tumor intensities. This method produces a potential cluster which is achieved through the hybrid of three unsupervised learning techniques. Hybrid cluster method segments the tumor region. Extended intensities are also segmented by this hybrid approach. Above methods are validated on MICCAI BraTs brain tumor dataset, this is a segmentation challenge dataset. Proposed hybrid algorithm is efficient and it's accuracy can be seen with testing parameters like Dice Overlap Index, Jaccard Tanimoto Coefficient Index, Mean Squared Error and Peak Signal to Noise Ratio. Dice OverlapIndex is 98%, Jaccard Index is 96 percent, Mean Squared Error is 0.06 and Peak Signal To Noise ratio is 18db. The performance of the suggested algorithm is compared to other state of the art.

关键词： Tumors Feature extraction Image segmentation Magnetic resonance imaging Indexes Image edge detection Organizations Self organization mapping (SOM) KMEAN Fuzzy C Mean (FCM) features feature extraction (FE) feature reduction (FR) feature selection (FS) MRI contour detection biggest blob area intensity hybrid segmentation confidence region (CR) contour detection (CD)

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A multi-parametric machine learning approach using authentication trees for the healthcare industry

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EXPERT SYSTEMS 2024年第2期41卷 e13202-e13202页

作者： Abunadi, Ibrahim Rehman, Amjad Haseeb, Khalid Alam, Teg Jeon, Gwanggil CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt Lab AIDA Riyadh Saudi Arabia Islamia Coll Peshawar Dept Comp Sci Peshawar Pakistan Prince Sattam Bin Abdulaziz Univ Coll Engn Dept Ind Engn Al Kharj Saudi Arabia Azad Inst Engn & Technol Azad puramChandrawal via Bangla Bazar & BijnourN Lucknow India Incheon Natl Univ Dept Embedded Syst Engn Incheon South Korea CCIS Prince Sultan Univ Incheon South Korea Incheon Natl Univ Incheon South Korea

The Internet of Health Things (IoHT) has grown in importance for developing medical applications with the support of wireless communication systems. IoHT is integrated with many sensors to capture the patients' records and transmits them to hospital centres for analysis and reporting. Controlling and managing health records has been addressed in several ways, however, it is noted that two key research problems for vital communication systems are reliability and reducing data loss. To enhance the sustainability of health applications and effectively use the network infrastructure when transferring sensitive data, this research provides a machine learning approach. Moreover, data collected from the IoHTs are protected and can be securely received for physical process in hospitals using authentication trees. Firstly, the undirected graphs are explored based on the multi-parametric machine learning approach to minimize the computation overheads and traffic congestion. Secondly, it evaluates the nodes' level behaviour over the heterogeneous traffic load with efficient identification of redundant links. Finally, in-depth analysis and simulation results have shown that the proposed protocol is more effective than existing approaches for data accuracy and security analysis.

关键词： data distribution health risks healthcare industry internet of things machine learning multi-parametric analysis security

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NC-ALG: Graph-Based Active Learning Under Noisy Crowd

NC-ALG: Graph-Based Active Learning Under Noisy Crowd

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International Conference on data Engineering

作者： Wentao Zhang Yexin Wang Zhenbang You Yang Li Gang Cao Zhi Yang Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications Key Lab of High Confidence Software Technologies Peking University Department of Data Platform TEG Tencent Inc. Beijing Academy of Artificial Intelligence Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active Learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed.

关键词： Crowdsourcing Costs Noise Predictive models Graph neural networks labeling Noise measurement

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Reliability Analysis for Electronic Devices Using Generalized Exponential Distribution

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IEEE ACCESS 2020年 8卷 108629-108644页

作者： Ali, Sajid Ali, Shafaqat Shah, Ismail Siddiqui, Ghazanfar Farooq Saba, Tanzila Rehman, Amjad Quaid I Azam Univ Dept Stat Islamabad 45320 Pakistan Quaid I Azam Univ Dept Comp Sci Islamabad 45320 Pakistan Prince Sultan Univ CCIS Artificial Intelligence & Data Analyt Lab Riyadh 11586 Saudi Arabia

Electronic devices are integral part of our life and modeling their lifetime is the most challenging and interesting field in reliability analysis. To investigate the failure behavior of electronic devices reliability analysis is commonly used. In the literature, however, it is reported that one in five electronic device failure is a result of corrosion and to save electricity and predict future failures, it is important to summarize the data by some flexible probability models. This will not only help the electronic companies, but also the users by providing them information about the maximum voltage level that a particular device can bear. This article deals with the reliability analysis of electronic devices under different voltages assuming modified generalized exponential distribution and beta generalized exponential distribution using the inverse power law rule. The parameters of the modified distribution are estimated assuming Bayesian inference to include prior information. Sensitivity of hyperparameters and selection of an appropriate probability model is also a part of this study.

关键词： Electronic devices beta generalized exponential distribution Bayesian analysis generalized exponential distribution inverse power law sensitivity analysis reliability data analysis voltage

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Reduced Reference Image Quality Assessment Technique Based on DWT and Path Integral Local Binary Patterns

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020年第4期45卷 3387-3401页

作者： Abbas, Naveed Saba, Tanzila Khan, Siraj Mehmood, Zahid Rehman, Amjad Tabasum, Rubby Islamia Coll Univ Dept Comp Sci Peshawar Pakistan CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt Lab Riyadh Saudi Arabia Univ Engn & Technol Dept Comp Engn Taxila 47050 Pakistan

In the prevailing technological advancements, image and video services are one of the primary concerns of researches around the world. The transmission of multimedia contents involved various types of attenuations such as blurring, ringing, contrast, and blocking. To evaluate the quality of the image or video, researchers presented various quality assessment techniques, including subjective and objective assessments. In this article, a robust reduced reference image quality assessment technique based on the objective assessment is presented, which considers the extracted features of the underlying image to evaluate its quality. The proposed method uses a discrete wavelet transform-based path integral local binary pattern histogram as extracted features. These features of the reference and testing image are compared by a Euclidean distance formula to generate a single value to evaluate the quality of the image. The qualitative and quantitative analysis of the proposed method is carried on three different image datasets, namely LIVE, TID2013, and CSIQ. The experimental results of the proposed method indicate that it produces robust performance in terms of performance evaluation metrics as compared with state-of-the-art methods.

关键词： Reduced reference image quality assessment Discrete wavelet transform Path integral local binary patterns RMSE

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Volumetric Segmentation of Brain Regions From MRI Scans Using 3D Convolutional Neural Networks

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IEEE ACCESS 2020年 8卷 103697-103709页

作者： Ramzan, Farheen Khan, Muhammad Usman Ghani Iqbal, Sajid Saba, Tanzila Rehman, Amjad Univ Engn & Technol UET Lahore Natl Ctr Artificial Intelligence Al Khawarizmi Inst Comp Sci KICS Intelligent Criminol Res Lab Lahore 54000 Pakistan Bahauddin Zakariya Univ Dept Comp Sci Multan 60700 Pakistan Prince Sultan Univ Coll Comp & Informat Sci CCIS Artificial Intelligence & Data Analyt AIDA Lab Riyadh 11586 Saudi Arabia

Automated brain segmentation is an active research domain due to the association of various neurological disorders with different regions of the brain, to help medical professionals in prognostics and diagnostics. Traditional techniques like atlas-based and pattern recognition-based methods led to the development of various tools for automated brain segmentation. Recently, deep learning techniques are outperforming classical state-of-the-art methods and gradually becoming more mature. Consequently, deep learning has been extensively employed as a tool for precise segmentation of brain regions because of its capability to learn the intricate features of the high-dimensional data. In this work, a network for the segmentation of multiple brain regions has been proposed that is based on 3D convolutional neural networks and utilizes residual learning and dilated convolution operations to efficiently learn the end-to-end mapping from MRI volumes to the voxel-level brain segments. This research is focused on the segmentation of up to nine brain regions including cerebrospinal fluid, white matter and gray matter as well as their sub-regions. Mean dice scores of 0.879 and 0.914 have been achieved for three and nine brain regions, respectively by using the data from three different sources. Comparative analysis shows that our network gives better dice scores for most of the brain regions than state-of-the-artwork. Moreover, the mean dice score of 0.903, obtained for eight brain regions segmentation with MRBrains18 dataset, is better than 0.876 which was achieved in the previous work.

关键词： Brain segmentation convolutional neural networks magnetic resonance imaging volumetric segmentation

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Compiler-Assisted Kernel Selection for FPGA-based Near-Memory Computing Platforms

Compiler-Assisted Kernel Selection for FPGA-based Near-Memor...

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Annual IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM)

作者： Veronia Iskandar Mohamed A. Abd El Ghany Diana Goehringer Chair of Adaptive Dynamic Systems Technische Universität Dresden Germany Electronics Department IES Lab German University in Cairo Egypt/ TU Darmstadt Germany Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig Germany

The speed of modern computing systems has improved significantly, thanks to advances in CMOS technology. However, the memory bandwidth of DRAM has not kept pace with these improvements in terms of latency and energy consumption, which is known as the memory wall [1]. FPGAs with high-bandwidth memory (HBM) provide significantly improved performance on memory-intensive tasks, such as graph processing and machine learning. By leveraging 3D-stacked DRAM memory on FPGAs, it is possible to realize the Near-Memory Computing (NMC) paradigm, which involves offloading some kernels to be processed close to the memory. While there have been many studies on NMC accelerators, there is no established method for determining which application kernels are suitable for execution near the HBM. To fully realize the potential of FPGA-HBM architectures, it is important to identify offloading candidates without relying on programmers' knowledge. However, this is a non-trivial task due to the complexity of modern applications. To address this issue, we propose a compiler-assisted tool-flow for the automatic selection of kernels to be offloaded.

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Auto-DOK: Compiler-Assisted Automatic Detection of Offload Kernels for FPGA-HBM Architectures

Auto-DOK: Compiler-Assisted Automatic Detection of Offload K...

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Euromicro Symposium on Digital System Design

The bandwidth improvement provided by high-bandwidth memory (HBM), and the capability of FPGAs to customize the processing and memory hierarchy, results in a considerable performance increase for memory-intensive work-loads such as graph processing, sorting, machine learning, and database analytics. Modern systems integrating 3D-stacked DRAM memory can be leveraged to realize the Near-Memory Computing (NMC) paradigm by offloading some computations to accelerators placed near the HBM. Although numerous studies have investigated efficient accelerators for FPGA-HBM platforms, researchers have not proposed a systematic way for identifying which application kernels are suitable for execution near the HBM. In this article, we propose compiler support for recognizing offloading candidates without any burden on programmers. Auto-DOK analyzes an application code based on criteria derived from the hardware design goals of FPGA-HBM platforms, and automatically identifies kernels suitable for offloading. We evaluate Auto-DOK on benchmarks ranging from microbenchmarks to real-world kernels. Our results show that Auto-DOK can correctly identify kernels and input sizes suitable for execution near the HBM, and prevents slowdown caused by incorrect offloading decisions for other workloads. Moreover, Auto-DOK operates at compile time with negligible overhead and without the need for expensive profiling.

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Performance Estimation and Prototyping of Reconfigurable Near-Memory Computing Systems

Performance Estimation and Prototyping of Reconfigurable Nea...

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International Conference on Field Programmable Logic and Applications

作者： Veronia Iskandar Mohamed A. Abd El Ghany Diana Goehringer Chair of Adaptive Dynamic Systems Technische Universität Dresden Germany Electronics Department IES Lab German University in Cairo Egypt TU Darmstadt Germany Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig Germany

The concept of near-memory computing (NMC) has emerged as a promising solution to address the memory wall challenges faced by future computing architectures. By utilizing modern systems that integrate 3D-stacked DRAM memory, the NMC paradigm minimizes unnecessary data movement between the memory subsystem and the CPU. FPGA vendors have incorporated 3D-stacked memories into their products to meet the increasing bandwidth requirements of memory-intensive applications, enabling FPGAs to compete with GPU solutions in terms of speed and energy efficiency. Recent NMC proposals focus on different data processing workloads, including graph processing and machine learning. This work addresses the research questions of how to leverage the full bandwidth of 3D-stacked high-bandwidth memory and how to facilitate the adoption of the near-memory computing paradigm.

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