300 research outputs found
Communication Technologies for Vehicles: Third International Workshop, Nets4Cars/Nets4Trains 2011 Oberpfaffenhofen, Germany, March 23-24, 2011 Proceedings
No full textThe Communication Technologies for Vehicles workshop series provides an international forum on latest technologies and research in the field of intra- and inter-vehicle communications in which to present original research results in all areas relating to communication protocols and standards, mobility and traffic models, experimental and field operational testing, and performance analysis
An Intellectual Review of the Travelogue “Taparwasni”: سفر نامہ ٹپرواسنی کا فکری جائزہ
No full text"Taparvasni" (Gypsy) is the second collection of Parveen Atif's travelogues, which came out in 1995 after a gap of eight years from his earlier collection "Kiran, Titli Aur Bagole". Parveen Atif traveled to different cities and wrote these travelogues by dividing them into different chapters. In this travelogue, visits to Luckhnow (India), Buenos Aires (Argentina), Amsterdam (Holland), Peking (China) and Hiroshima (Japan) have been described; whereas visits to London (UK) and Damascus (Syria) have also been mentioned. The purpose of the visits included in this travelogue contains engagements in sports (hockey) and allied official duties. This article presents an intellectual review of the said travelogues highlighting their literary, observational and cultural importance.
Reference:
Parveen Atif,tapawasni,Lahore,Sang e meel publications.1995,p 9.
Ghafoor shah qasim ,Dr,Pakistani adab ky so saal ,Lahore: book talk publishers ,1995,p:15.
Parveen Atif,Tapar wasni ,P: 10.
Anwar sadeed,Dr,Urdu adab main safarnama, Lahore,Maghrabi Pakistan Urdu Acedmy ,p :40.
Parveen Atif,Taparwasni,p, 112.
Khalid Mehmood ,Urdu safernamo ka tanqeedi muatliya,Lahore,maktaba jamaiya ,2011,p:52.
Parveen Atif,Taparwasni,p, 44.
Aihsan ,Zulfiqar Ali,Azadi ky baad safernama miN jns nigari ka ruhjaan ,Lahore:Maghrabi PAKISTAN Urdu,2008,P:201.
Parveen Atif,Taparwasni,p, 90.
Sadaf FATIMA,Dr,Khawateen ky Urdu safernaamo ka tehqeeqi mutaliya,Lahore:Anjuman taraqi Urdu,P: 8
Parveen Atif,Taparwasni,p, 120
 
Data-driven classification and explainable-AI in the field of lung imaging
No full textDetecting lung diseases in medical images can be quite challenging for radiologists. In some cases, even experienced experts may struggle with accurately diagnosing chest diseases, leading to potential inaccuracies due to complex or unseen biomarkers. This review paper delves into various datasets and machine learning techniques employed in recent research for lung disease classification, focusing on pneumonia analysis using chest X-ray images. We explore conventional machine learning methods, pretrained deep learning models, customized convolutional neural networks (CNNs), and ensemble methods. A comprehensive comparison of different classification approaches is presented, encompassing data acquisition, preprocessing, feature extraction, and classification using machine vision, machine and deep learning, and explainable-AI (XAI). Our analysis highlights the superior performance of transfer learning-based methods using CNNs and ensemble models/features for lung disease classification. In addition, our comprehensive review offers insights for researchers in other medical domains too who utilize radiological images. By providing a thorough overview of various techniques, our work enables the establishment of effective strategies and identification of suitable methods for a wide range of challenges. Currently, beyond traditional evaluation metrics, researchers emphasize the importance of XAI techniques in machine and deep learning models and their applications in classification tasks. This incorporation helps in gaining a deeper understanding of their decision-making processes, leading to improved trust, transparency, and overall clinical decision-making. Our comprehensive review serves as a valuable resource for researchers and practitioners seeking not only to advance the field of lung disease detection using machine learning and XAI but also from other diverse domains
Intra-vehicular verification and control: a two-pronged approach
No full textModern vehicles are equipped with hundreds of embedded networked components with computational, sensory and actuation powers. Reliable functioning and interaction of these components are vital for the safety of the vehicle and its passengers. We present an architecture that deals with the intra-vehicular network at both component and system levels. At the component level, our technique formally verifies compatibility of each component with the rest of the system. At the system level, we provide means to define overall behaviour by using first-order logic rules in an ontological space. Overall, we eliminate the hazards associated with integrating heterogeneous components in a car network domain and enable a knowledgeable user to define network behaviour easily
Estimation of Distribution of Income in Pakistan, Using Micro Data
No full textIncome distribution entered the post war discussion of economic development fairly late. Until the 1960s much of the focus was on industrialisation and the need for capital accumulation. Pakistan was no exception as in the early 60s economic expansion became the main target and means to political identity. Rapid population growth associated with steep decline in mortality demanded acceleration of production to keep pace. Overall aggregate expansion was much faster than before but without benefit for the poor. In that context emerged a new professional interest in income distribution. Haq’s (1964) study was one of the oldest studies conducted to measure inequality in personal income distribution in the high income brackets in the urban areas of Pakistan. The main objective of the author was to present the income distribution pattern in terms of the relative shares of different income groups as well as in terms of Pareto coefficients and concentration ratio during the period 1948-49 to 1957-58 for which published tax data was available. While recognising the limitations of the data used, the author went on to calculate various measures of income inequality including Pareto coefficient and Lorenz curve. The author also made comparison of Pakistan’s income distribution with U.S.A. and U.K.
Smart COVID-3D-SCNN: A novel method to classify x-ray images of COVID-19
No full textThe outbreak of the novel coronavirus has spread worldwide, and millions of people are being infected. Image or detection classification is one of the first application areas of deep learning, which has a significant contribution to medical image analysis. In classification detection, one or more images (detection) are usually used as input, and diagnostic variables (such as whether there is a disease) are used as output. The novel coronavirus has spread across the world, infecting millions of people. Early-stage detection of critical cases of COVID-19 is essential. X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early. For extracting the discriminative features through these modalities, deep convolutional neural networks (CNNs) are used. A siamese convolutional neural network model (COVID-3D-SCNN) is proposed in this study for the automated detection of COVID-19 by utilizing X-ray scans. To extract the useful features, we used three consecutive models working in parallel in the proposed approach. We acquired 575 COVID-19, 1200 non-COVID, and 1400 pneumonia images, which are publicly available. In our framework, augmentation is used to enlarge the dataset. The findings suggest that the proposed method outperforms the results of comparative studies in terms of accuracy 96.70%, specificity 95.55%, and sensitivity 96.62% over (COVID-19 vs. non-COVID19 vs. Pneumonia)
Early Diagnosis of Alzheimer’s Disease Based on Convolutional Neural Networks
No full textAlzheimer’s disease (AD) is a neurodegenerative disorder, causing the most common dementia in the elderly peoples. The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA. Magnetic resonance imaging (MRI) is the leading modality used for the diagnosis of AD. Deep learning based approaches have produced impressive results in this domain. The early diagnosis of AD depends on the efficient use of classification approach. To address this issue, this study proposes a system using two convolutional neural networks (CNN) based approaches for an early diagnosis of AD automatically. In the proposed system, we use segmented MRI scans. Input data samples of three classes include 110 normal control (NC), 110 mild cognitive impairment (MCI) and 105 AD subjects are used in this paper. The data is acquired from the ADNI database and gray matter (GM) images are obtained after the segmentation of MRI subjects which are used for the classification in the proposed models. The proposed approaches segregate among NC, MCI, and AD. While testing both methods applied on the segmented data samples, the highest performance results of the classification in terms of accuracy on NC vs. AD are 95.33% and 89.87%, respectively. The proposed methods distinguish between NC vs. MCI and MCI vs. AD patients with a classification accuracy of 90.74% and 86.69%. The experimental outcomes prove that both CNN-based frameworks produced state-of-the-art accurate results for testing
Hydraulic simulations to evaluate and predict design and operation of the Chashma Right Bank Canal
No full textIrrigation systems / Irrigation canals / Flow control / Velocity / Canal regulation techniques / Hydraulics / Simulation models / Design / Operations / Crop-based irrigation / Distributary canals / Water delivery / Policy / Protective irrigation / Water allocation / Water requirements / Sedimentation / Water distribution / Equity / Water conveyance / Pakistan / Chashma Right Bank Canal
WASTE MATERIALS AS SUBSTRATES IN VERTICAL FLOW CONSTRUCTED WETLANDS TREATING DOMESTIC WASTEWATER
No full textAn Integrative Health Care Informatics Model for Early Detection of Skin Cancer
No full textBecause lesion features and detection backgrounds are complex, automatic lesion detection in dermoscopy images is fraught with difficulties. Using more significant and more complicated models has been the primary strategy used by previous methods to improve detection accuracy. However, these methods frequently miss significant variations within classes and commonalities between classes in lesion characteristics. This research gap restricts our comprehension of the subtle differences within lesion classes and the commonalities among several classes. The use of bigger model sizes further complicates implementing algorithms in real-world contexts. Consequently, studies that delve further into the underlying intricacies of lesion features and investigate novel ways to overcome these obstacles while guaranteeing the scalability and applicability of the detection algorithms are desperately needed. This research aims to tackle the issue of insufficient annotated data in skin cancer diagnosis by proposing a new and innovative 3D neural network (NN) model based on deep learning techniques. The design of our model is tailored to specifically detect several types of skin cancer, such as Melanoma, Nevus, Actinic keratosis, and Dermatofibroma. To address the limitations of the available data, we utilise an augmentation technique to increase the size of the dataset. This helps to improve the model\u27s ability to handle different scenarios and make accurate predictions while avoiding overfitting. By doing thorough experiments, we have achieved an impressive accuracy rate of 93.30% in distinguishing Actinic keratosis from Nevus. This demonstrates the efficacy of our suggested method in properly recognising various forms of skin lesions
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