1,721,603 research outputs found
Visible light backscattering with applications to the Internet of Things: State-of-the-art, challenges, and opportunities
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SINO-INDIAN MILITARY AND BORDER DISPUTES INCLUDING THE 1962 WAR: IMPLICATIONS AND LESSONS FOR PAKISTAN
No full textThe study aims to explore China-India disputes, their implications and lessons for Pakistan. Since 1960, China and India have border disputes that have led to several clashes. The genesis of border tensions can be attributed to India’s aggressive posture through the Forward Policy. China does not accept the McMahon Line and claims Arunachal Pradesh. India does not accept the Line of Actual Control (LAC) in the Western Sector and claims the complete Aksai Chin as part of India. Unfavourable settlement of the dispute in the Eastern Sector has grave strategic implications. Aksai Chin in the West is at the crossroads of politically sensitive regions and holds military significance as a launch pad. Despite years of conflict and unsettled border issues, both countries have remained engaged in economic activities, with China being India’s top trading partner since 2008. Border disputes based on competing claims, India’s approach towards neighbours, and the nature of relations are likely to be competitive. Both countries have signed multiple agreements to manage their border disputes; however, implementation remains limited due to mistrust.
Bibliography Entry
Baig, Sheraz, Syed Umad Ul Hassan and Habib Ullah Khan Afridi. 2025. "Sino-Indian Military and Border Disputes including the 1962 War: Implications and Lessons for Pakistan." Margalla Papers 29 (2): 17-32
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Fintech applications in Islamic finance: AI, machine learning, and blockchain techniques
No full textIn the realm of Islamic finance, a pivotal challenge looms-the escalating complexity of investment decisions, macroeconomic analyses, and credit evaluations. In response, we present a groundbreaking solution that resonates with the rapidly evolving fintech era. Fintech Applications in Islamic Finance: AI, Machine Learning, and Blockchain Techniques offers a compelling repository of knowledge, meticulously curated by renowned editors Mohammad Irfan, Seifedine Kadry, Muhammad Sharif, and Habib Ullah Khan. At the heart of this challenge lies a convergence of technologies-artificial intelligence (AI), machine learning (ML), and blockchain-that have revolutionized financial services. Our book unravels the symbiotic relationship between these innovations and the intricate world of Islamic finance. As institutions strive to navigate this landscape, our solution emerges as a guiding light. By delving into AI's predictive power and ML's capacity to analyze vast datasets, the book empowers financial institutions to automate processes, enhance efficiency, and make informed decisions. As the global Muslim population grows, the demand for Islamic financial services intensifies, presenting a unique growth opportunity. However, with growth comes the challenge of managing resources, regulations, and strategies. This book serves as an invaluable guide for governments, academic institutions, and industry players, offering insights into AI/ML's transformative potential within Islamic fintech. From financial monitoring to blockchain applications, this compendium addresses diverse topics, equipping researchers, academicians, industrialists, and investors with the knowledge to navigate the intricacies of modern Islamic finance. Fintech Applications in Islamic Finance: AI, Machine Learning, and Blockchain Techniques is a call to action, an exploration of innovation, and a guide for both academia and industry. In an era where AI, ML, and blockchain reshape finance, this book stands as a beacon of knowledge, ushering Islamic finance into a realm of unprecedented efficiency and insight. As we invite readers to embark on this transformative journey, we illuminate the path to a future where technology and tradition converge harmoniously
Pedestrian Classification in Traffic Scenes: Assessing Deep Learning Models on Imbalanced, Augmented, and Synthetic Data
Get PDFRecent advancements in pedestrian detection have primarily focused on object detection frameworks. However, this thesis investigates a classification-based approach. Specifically, it addresses two tasks: (1) predicting the presence of zero, one, or multiple pedestrians in an image, and (2) classifying whether an image was taken during the day or night. A subset of the EuroCity Persons dataset was used, covering diverse lighting conditions and urban scenes. Five deep learning models were evaluated: four convolutional neural networks (MobileNetV2, EfficientNetV2, ConvNeXt, and NASNetMobile) and one transformer-based model (Vision Transformer). Transfer learning was applied, and techniques such as data augmentation and Variational Autoencoder-based synthetic data generation were used to address dataset imbalance and improve model performance. All models achieved high accuracy on the day/night classification task. In contrast, their performance on pedestrian classification was weaker, likely due to task complexity and dataset characteristics. No clear difference was observed between the convolutional neural networks and Vision Transformer models, suggesting that dataset limitations were the main factor affecting performance. These findings indicate that datasets designed for pedestrian detection are not always suitable for classification, as the two tasks place different requirements on the data
Automated Detection of Empty Totes in Warehouse Environment
Get PDFEfficient inventory management is crucial in various industrial and
logistics sectors. The use of automated guided vehicles and robotic bin
picking systems has become increasingly important in modern manufacturing
and distribution operations. These systems often rely on computer
vision and Machine Learning (ML) techniques to detect, identify, and
track objects of interest, such as pallets and plastic totes. In this study, a
manual work task in one of Norway’s largest automation warehouses will
be automated using machine vision. Plastic totes in this specific warehouse
needs to be confirmed manually by an operator if the tote is empty
or not. This operation is repeated an average of 15,000 times per day,
and measurements show that each manual confirmation can take up to
two seconds. Automating this task with machine vision presents a good
opportunity to reduce operational time and effort. To solve this task,
we will explore three different Convolutional Neural Network (CNN) as
well as two Red, Green, Blue (RGB) statistical models to classify if the
tote is empty or not. RGB statistical models are based on comparison
of average pixel color intensity. Our CNN models include one custom 7-
layer small model, one MobileNetV2, and one Visual Geometry Group
model VGG16 (VGG16). The last two models will be based on transfer
learning. The image dataset has been collected from the live production
environment. From this collection, a balanced dataset of 400 images has
been used to train our CNN. We will emphasize on a light weight solution
with little delay. All CNNs achieve accuracy above 98% The MobileNetV2
shows very promising results, achieving accuracy up to 100% with an inference
time of only 22ms. The RGB methods can not show the same
accuracy as the CNN but are by far the smallest in size. Our result indicates
that CNN should be well suited to predict if the totes are empty
or not. The high accuracy achieved by all CNN models, especially the
fast and lightweight MobileNetV2, demonstrates CNN is highly suitable
for automating the empty/non-empty tote classification task in the warehouse
environment. Offering a significant improvement in efficiency over
manual inspection
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