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Service Oriented Architecture Concepts Technology And Design

Bringing in the tides. From closing down to opening up delta polders via Tidal River Management in the southwest delta of Bangladesh

The article analyzes Tidal River Management in Bangladesh from a social learning perspective. Four cases were investigated using participatory assessment. Knowledge acquisition through transformations in the Tidal River Management process was explored as an intended learning outcome. The study finds that social learning occurred more prominently at the individual stakeholder level and less at the collective level. For Tidal River Management to be responsive and sustainable, especially in times of increased uncertainty and climate vulnerability, more attention needs to be paid to coordination and facilitation of multi-level learning that includes all stakeholders.

Social learning for adaptive delta management: Tidal River Management in the Bangladesh Delta

Plant diseases pose a major challenge in the agricultural sector, which affects plant development and crop productivity. Sugarcane farming is a highly organized part of farming. Owing to the desirable condition for sugarcane cultivation, India stands among the second largest producers of sugarcane over the globe. At the same time, sugarcane gets easily affected by multifarious diseases which significantly influence crop productivity. The recently developed computer vision (CV) and deep learning (DL) models with an effective design can be employed for the detection and classification of diseases in sugarcane plant. The disease detection in sugarcane plant is not accurate in the existing techniques. This paper presents a quantum behaved particle swarm optimization based deep transfer learning (QBPSO-DTL) model for sugarcane leaf disease detection and classification which produces high accuracy. The proposed QBPSO-DTL method is designed and trained for the prediction of diseased leaf images. The proposed QBPSO-DTL technique encompasses the design of optimal region growing segmentation to determine the affected regions in the leaf image. In addition, the SqueezeNet model is employed as a feature extractor and the deep stacked autoencoder (DSAE) model is applied as a classification model. Finally, the hyperparameter tuning of the DSAE model is carried out by using the QBPSO algorithm. For demonstrating the enhanced outcomes of the QBPSO-DTL approach, a wide range of experiments were implemented and the results ensured the improvements of the QBPSO-DTL model.

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Quantum Behaved Particle Swarm Optimization-Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification

Purpose – The main aim of this paper is to investigate the potential of Cloud Computing as a multilayer integrative collaboration space for knowledge acquisition, nurturing and sharing. The paper will pinpoint benefits and challenges of Cloud Computing in satisfying the new techno-sociological requirements of the knowledge society through the provision of information technology (IT) green services. Furthermore, the article calls for the engagement of researchers to generate additional discussion and dialog in this emerging and challenging area. Design/methodology/approach – The paper applies a conceptual analysis to explore the utilization of the Cloud ecosystem as a new platform for knowledge management (KM) technologies characterized by environmental and economic benefits. Findings – This paper reveals the emergence of a new layer in the Cloud stack known as Knowledge Management-as-a-Service. The article discusses how KM has the opportunity to evolve in synergy with Cloud Computing technologies using th...

Cloud computing: a collaborative green platform for the knowledge society

Alzheimer's disease is characterized by the presence of abnormal protein bundles in the brain tissue, but experts are not yet sure what is causing the condition. To find a cure or aversion, researchers need to know more than just that there are protein differences from the usual; they also need to know how these brain nerves form so that a remedy may be discovered. Machine learning is the study of computational approaches for enhancing performance on a specific task through the process of learning. This article presents an Alzheimer's disease detection framework consisting of image denoising of an MRI input data set using an adaptive mean filter, preprocessing using histogram equalization, and feature extraction by Haar wavelet transform. Classification is performed using LS-SVM-RBF, SVM, KNN, and random forest classifier. An adaptive mean filter removes noise from the existing MRI images. Image quality is enhanced by histogram equalization. Experimental results are compared using parameters such as accuracy, sensitivity, specificity, precision, and recall.

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Machine Learning and Image Processing Enabled Evolutionary Framework for Brain MRI Analysis for Alzheimer's Disease Detection

Breast cancer is the most lethal type of cancer for all women worldwide. At the moment, there are no effective techniques for preventing or curing breast cancer, as the source of the disease is unclear. Early diagnosis is a highly successful means of detecting and managing breast cancer, and early identification may result in a greater likelihood of complete recovery. Mammography is the most effective method of detecting breast cancer early. Additionally, this instrument enables the detection of additional illnesses and may provide information about the nature of cancer, such as benign, malignant, or normal. This article discusses an evolutionary approach for classifying and detecting breast cancer that is based on machine learning and image processing. This model combines image preprocessing, feature extraction, feature selection, and machine learning techniques to aid in the classification and identification of skin diseases. To enhance the image’s quality, a geometric mean filter is used. AlexNet is used for extracting features. Feature selection is performed using the relief algorithm. For disease categorization and detection, the model makes use of the machine learning techniques such as least square support vector machine, KNN, random forest, and Naïve Bayes. The experimental investigation makes use of MIAS data collection. This proposed technology is advantageous for accurately identifying breast cancer disease using image analysis.

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Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis

The aim of this study is to evaluate infected leaf disease images. Precision agriculture's automatic leaf disease detection system employs image acquisition, image processing, image segmentation, feature extraction, and machine learning techniques. An automated disease detection system offers the farmer with a fast and accurate diagnosis of the plant disease. Automation of plant leaf disease detection system is essential for accelerating crop diagnosis. Using machine learning and image processing, this paper describes a framework for detecting leaf illness. An image of a leaf can be used as an input for this framework. To begin, leaf photographs are preprocessed in order to remove noise from their images. The mean filter is used to filter out background noise. Histogram equalization is used to enhance the quality of the image. The division of a single image into multiple portions or segments is referred to as segmentation in photography. It assists in establishing the boundaries of the image. Segmenting the image is accomplished using the K-Means approach. Feature extraction is carried by using the principal component analysis. Following that, images are categorized using techniques such as RBF-SVM, SVM, random forest, and ID3.

Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection

Stock market prediction based on statistical data using machine learning algorithms

In this paper we first look at data mining applications in safety measures and their suggestions for privacy. After that we then inspect the idea of privacy and give a synopsis of the developments particularly those on privacy preserving data mining. We then present an outline for research on confidentiality and data mining.

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Abu Sarwar Zamani

Papers

Machine Learning and Image Processing Enabled Evolutionary Framework for Brain MRI Analysis for Alzheimer's Disease Detection

Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis

Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection

Stock market prediction based on statistical data using machine learning algorithms

Data Mining For Security Purpose & Its Solitude Suggestions