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"Umer, Muhammad"
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Architecture and Design of the Linux Storage Stack
Master the design and structure of Linux storage stack and explore its sophisticated architecture Purchase of the print or Kindle book includes a free PDF eBook
Key Features
Explore the virtual file system (VFS) and how it serves as an abstraction layer for the actual file systemUnderstand how the block layer acts as an intermediary between file systems and physical storageDiscover the physical layout and protocols linked with storage media
Book Description
The Linux storage stack serves as a prime example of meticulously coordinated layers. Embark on a journey through the kernel code with Architecture and Design of the Linux Storage Stack, crafted for anyone seeking in-depth knowledge about the layered design of Linux storage and its landscape. You’ll explore the Linux storage stack and its various concepts. You’ll unlock the secrets of the virtual filesystem and the actual filesystem and the differences in their implementation, the role of the block layer, the Multi-Queue and Device Mapper frameworks, I/O schedulers, physical storage layout, and how to analyze all the layers in the storage stack. By the end of this book, you’ll be acquainted with how a simple I/O request from a process travels down through all the layers and ends up in physical storage.
What you will learn
Understand the role of the virtual filesystemExplore the different flavors of Linux filesystems and their key conceptsManage I/O operations to and from block devices using the block layerDeep dive into the Small Computer System Interface (SCSI) subsystem and the layout of physical devicesGauge I/O performance at each layer of the storage stackDiscover the best storage practices
Who this book is for
This book is for system and storage administrators, engineers, linux professionals, linux community in general, and anyone looking to expand their understanding of Linux and its storage landscape. Prior knowledge of Linux operating system is a must.
eBook
Socio-Economic development and sustainable development goals: a roadmap from vulnerability to sustainability through financial inclusion
Sustainable Development Goals (SDGs) highlight the importance of poverty reduction, and call for policy implementation that leads to the socio-economic development of impoverished people. However, there is a lack of knowledge about assessing individual-level socio-economic development, and how financial inclusion through microfinance can contribute to it. Therefore, the role of commercially operated Microfinance Banks (MFBs) is also considered to be controversial in the literature. This study assesses the overall socio-economic development by considering different sustainable livelihoods, multidimensional poverty, living standards, and social development measures. Thus, the Multidimensional Poverty Index (MPI), and Living Standard Index (LSI) have been estimated to gauge poverty and improvements in living standards. Data comprising 503 customers of MFBs, and 500 control respondents, has been gathered through a survey to signify this impact for two years. This paper substantiates that the microfinance obtained from MFBs contributes positively towards sustainable livelihoods, multidimensional poverty reduction, and living standards. However, microfinance does not contribute to social development. Impoverished people, mainly women living in urban areas, reap more benefits from microfinance, than their rural counterparts. Overall, financial inclusion shall be a gateway to achieve the SDGs in the long run through the socio-economic development of an impoverished segment of the society.
Journal Article
Brain tumor classification in MRI image using convolutional neural network
Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Recent progress in the field of deep learning has helped the health industry in Medical Imaging for Medical Diagnostic of many diseases. For Visual learning and Image Recognition, task CNN is the most prevalent and commonly used machine learning algorithm. Similarly, in our paper, we introduce the convolutional neural network (CNN) approach along with Data Augmentation and Image Processing to categorize brain MRI scan images into cancerous and non-cancerous. Using the transfer learning approach we compared the performance of our scratched CNN model with pre-trained VGG-16, ResNet-50, and Inception-v3 models. As the experiment is tested on a very small dataset but the experimental result shows that our model accuracy result is very effective and have very low complexity rate by achieving 100% accuracy, while VGG-16 achieved 96%, ResNet-50 achieved 89% and Inception-V3 achieved 75% accuracy. Our model requires very less computational power and has much better accuracy results as compared to other pre-trained models.
Journal Article
The Critical Role of Zinc in Plants Facing the Drought Stress
Drought stress affects plant growth and development by altering physiological and biochemical processes resulting in reduced crop productivity. Zinc (Zn) is an essential micronutrient that plays fundamental roles in crop resistance against the drought stress by regulating various physiological and molecular mechanisms. Under drought stress, Zn application improves seed germination, plant water relations, cell membrane stability, osmolyte accumulation, stomatal regulation, water use efficiency and photosynthesis, thus resulting in significantly better plant performance. Moreover, Zn interacts with plant hormones, increases the expression of stress proteins and stimulates the antioxidant enzymes for counteracting drought effects. To better appraise the potential benefits arising from optimum Zn nutrition, in the present review we discuss the role of Zn in plants under drought stress. Our aim is to provide a complete, updated picture in order to orientate future research directions on this topic.
Journal Article
Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets
Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.
Journal Article
Development of AI-Augmented optimization technique for analysis & prediction of modal mix in road transportation
Transport sector contribution to global emissions is a known fact, however, the mitigation path to achieve nationally determined goals for carbon reduction is often not specified, A simplified technique based on minimax optimization using Grey relational grade and Random forest narrows down on most contributing input variables from twelve road transport modes. This is a region-specific, scenario-based technique applied to north Punjab, Province of Pakistan that first categorizes modes based on their emission and then integrates with AI modeling using Deep Neural Network to develop sustainable trade-offs for carbon reduction. The output parameter translates the problem into a systematic iterative technique that predicts optimization options with different scenarios to bring out an environment-friendly transport mix. A 25% reduction applied to the five most emission-releasing modes like Diesel Light and Heavy Duty vehicles, Gas Light and heavy-duty vehicles, and Gas-Cars results in 16.54 MT of Carbon dioxide which is 54.35% reduced to the predicted 36.24 MT for the year 2044. Similarly in another scenario replacing 25% Gas and Diesel Light Duty vehicles respectively by adding 50% Petrol Light Duty vehicles leads to 18.94 MT of emissions which brings the emission value in 2044 at par with emission releases of the year 2014. The technique offers a forward path that allows environment-friendly modal mix combinations based on business-as-usual to offer transport mix solutions for carbon reduction. It is a generalized model that is based on a customized transport mix. Future studies can also be applied to intermodal tradeoffs like rail, air, waterways, etc.
Journal Article
Systemic risk spillover between the stock market and banking deposits: Evidence from a sustainability perspective in the South Asian countries
This research explores the link between stock markets and banking deposits in South Asian (Pakistan, India, Sri Lanka, Nepal) countries. This study empirically examines the systemic risk potential of financial institutions in South Asia using current systemic risk statistics. Yearly data on stock prices and banking deposits from January 2000 to December 2020 were analyzed using a two-stage process. In the first phase, we measure VaR (value at risk), and in the second step, we measure the DCC GARCH model for our empirical analysis. The study findings reveal systemic risk spillover between the stock markets of South Asian countries and the relevant country’s banking system deposits. The policymakers can use our study findings to create a more sustainable financial sector.
Journal Article