In early 2020, the Coronavirus triggered a global pandemic, with roughly 28.7 million infections and 55.7 lakh deaths reported worldwide. Since the less restrictions by the government across the globe for the economic...
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ISBN:
(数字)9798331525439
ISBN:
(纸本)9798331525446
In early 2020, the Coronavirus triggered a global pandemic, with roughly 28.7 million infections and 55.7 lakh deaths reported worldwide. Since the less restrictions by the government across the globe for the economic improvement for the government as well as people and less obeying COVID regulations by the people and lethargic attitude number of cases may rise again. The existing test for COVID is RT-PCR however it takes in the range of 6 to 9 hours' time for getting the result and owing to less sensitivity it offers superior false negative results. X-ray imaging is a popular technique to visualize the lung disease impact and manual diagnosis is more error prone even with experts so a cascaded system using deep learning model like pre-trained and CNN model and many other deep learning model. Although they have a higher accuracy, some questions remain unanswered, such as whether the CNN based model performs better when the CXR input on the lung *** this paper, we employ ROI to segment the lung using U-net and RESNET-50 to train the segmented images, and then use grad-CAM (Class Activation Mapping) to emphasize which regions the neural network considers essential for classification in each segmented example.
Nanoparticle exposure induces morphological changes to the human cell surface, necessitating robust methods for quantitative analysis. Scanning electron microscopy (SEM) provides high-resolution imaging of these surfa...
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Self-supervised graph representation learning has driven significant advancements in domains such as social network analysis, molecular design, and electronics design automation (EDA). However, prior works in EDA have...
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The higher education landscape in Bangladesh has witnessed unbelievable amount of growth throughout the years yet remains a challenging domain for students due to intense competition and socio-structural disparities. ...
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ISBN:
(数字)9798350357509
ISBN:
(纸本)9798350357516
The higher education landscape in Bangladesh has witnessed unbelievable amount of growth throughout the years yet remains a challenging domain for students due to intense competition and socio-structural disparities. This paper explores a dataset collected from 15 public and private universities across Bangladesh and investigates the key components which influences undergraduate admissions in public and private universities. Moreover, statistical analysis is elaborately used to uncover patterns in academic inclinations, behavioural trends and develop predictive models. Afterwards a correlation heatmap was created to explore the interdependent relationships between the features. The machine learning models were trained with the correlated features achieving estimated prediction for XGBoost, Random Forest and Support Vector Machine (SVM). Using these models, we obtained an accuracy of 85.83% for Random Forest, whereas we obtained 85% for both XGBoost and Support Vector Machine (SVM). Furthermore, to enhance interpretability, explainable AI techniques such as SHAP (Shapley Additive Explanation) and LIME (Local Interpretable Model-agnostic Explanations) were applied to obtain a better visualization of the predicted models. To conclude, this study presents a comprehensive analysis highlighting the role of academic performance, socioeconomic background and behavioural aspects of a student's undergraduate admission journey through predictive modelling and explainable AI techniques. Further research would require a more diverse and better dataset with more features of behavioural trends of undergraduate students from Bangladesh.
Time-Sensitive Networking (TSN) has recently become an important standard to support time-sensitive reliable low-latency data transmission in industrial and real-time applications. This work explores the use of differ...
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ISBN:
(数字)9798331527549
ISBN:
(纸本)9798331527556
Time-Sensitive Networking (TSN) has recently become an important standard to support time-sensitive reliable low-latency data transmission in industrial and real-time applications. This work explores the use of different traffic shaping algorithms, namely, credit-based shaping (CBS) and time-aware shaping (TAS) alongside frame preemption-a TSN instance which gives precedence to high-importance frames by means of lower-priority traffic interruption-to extend TSN's traffic management capabilities. By controlling data flow and allowing for high priority packets to pass through unimpeded, traffic shaping algorithms serve an essential function in making the network more efficient and reducing congestion rates. The work compares these algorithms in isolation and also in combination over a network model, comprising of four switches and three end users for each switch, using the OMNeT++ simulation environment. This study aims to contribute insights into optimal shaping techniques for TSN, enhancing its suitability for critical network applications.
Semi-supervised learning (SSL) leverages abundant unlabeled data alongside limited labeled data to enhance learning. As vision foundation models (VFMs) increasingly serve as the backbone of vision applications, it rem...
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Quantum technologies are rapidly advancing as image classification tasks grow more complex due to large image volumes and extensive parameter updates required by traditional machine learning models. Quantum Machine Le...
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As the Internet connected devices and cyber– physical systems (CPS) are exponentially increasing, edge computing has become a crucial enabler to support real time processing in high-speed networks (HSNs). However, in...
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ISBN:
(数字)9798331501488
ISBN:
(纸本)9798331501495
As the Internet connected devices and cyber– physical systems (CPS) are exponentially increasing, edge computing has become a crucial enabler to support real time processing in high-speed networks (HSNs). However, integrating edge computing into broadband infrastructure is a challenging problem and that includes mitigating security risks such as DDoS and time-delay attacks and keeping ultra-low latency. Explored for this survey are some recent technologies on how to detect attacks and optimize latency on the edge, focusing on forms of deep learning and bioinspired approaches. It analyzes the lack of effectiveness in traditional mitigation strategies and represents new means, based on the use of ensemble learning, dimensionality reduction and hybrid models. Distributed edge infrastructures are specially emphasized on real time implementation, computational overhead and security. This paper attempts to contribute valuable insight for researchers developing robust, scalable, and intelligent edge-based solutions in high-speed networking environments by consolidating recent methodologies and their outcomes.
Prompt privacy is crucial, especially when using online large language models (LLMs), due to the sensitive information often contained within prompts. While LLMs can enhance prompt privacy through text rewriting, exis...
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The application of deep learning in agricultural tasks, such as ripeness detection of fruits has undergone rapid advancements. However, the real time challenges faced by these models under adverse imaging conditions r...
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ISBN:
(数字)9798331537555
ISBN:
(纸本)9798331537562
The application of deep learning in agricultural tasks, such as ripeness detection of fruits has undergone rapid advancements. However, the real time challenges faced by these models under adverse imaging conditions remain underexplored. This study evaluates the performance of You Only Look Once (YOLOv8) models for strawberry ripeness detection across four imaging scenarios: normal, blurred, darkened, and noisy conditions. A comprehensive dataset was prepared, incorporating diverse environmental challenges to simulate real-world conditions. The YOLOv8 model was trained and validated on each dataset, with performance evaluated using metrics such as precision, recall, mean Average Precision at Intersection over Union threshold 50% (mAP@50), and mAP across thresholds from 50% to 95% (mAP@50-95). Results reveal substantial variations in detection accuracy across conditions, highlighting the model’s sensitivity to image quality degradation, particularly in blurry and noisy scenarios. Despite struggling in blurry and noisy conditions, YOLOv8 demonstrated robust detection capabilities in normal and darkened conditions. This study provides valuable insights into the capabilities and limitations of current object detection models in strawberry cultivation and provides future directions for further scaling and enhancing the model into a cohesive setup for automated harvesting. These findings contribute to the development of more reliable machine learning systems in the field of agricultural automation
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