Browsing by Author "Deka, Lipika"

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    Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges
    (MDPI, 2024-02-09) Hasan, Jasmin; Mohammed Saeed, Safiya; Deka, Lipika; Uddin, Md Jasim; Das, Diganta B.
    The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. The widespread use of machine learning (ML) and mathematical modeling (MM)-based techniques is widely acknowledged. These two approaches have fueled the advancement in cancer research and eventually led to the uptake of telemedicine in cancer care. For diagnostic, prognostic, and treatment purposes concerning different types of cancer research, vast databases of varied information with manifold dimensions are required, and indeed, all this information can only be managed by an automated system developed utilizing ML and MM. In addition, MM is being used to probe the relationship between the pharmacokinetics and pharmacodynamics (PK/PD interactions) of anti-cancer substances to improve cancer treatment, and also to refine the quality of existing treatment models by being incorporated at all steps of research and development related to cancer and in routine patient care. This review will serve as a consolidation of the advancement and benefits of ML and MM techniques with a special focus on the area of cancer prognosis and anticancer therapy, leading to the identification of challenges (data quantity, ethical consideration, and data privacy) which are yet to be fully addressed in current studies.
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    Characterising Payload Entropy in Packet Flows
    (2024-04-29) Kenyon, Anthony; Deka, Lipika; Elizondo, David
    Accurate and timely detection of cyber threats is critical to keeping our online economy and data safe. A key technique in early detection is the classification of unusual patterns of network behaviour, often hidden as low-frequency events within complex time-series packet flows. One of the ways in which such anomalies can be detected is to analyse the information entropy of the payload within individual packets, since changes in entropy can often indicate suspicious activity - such as whether session encryption has been compromised, or whether a plaintext channel has been co-opted as a covert channel. To decide whether activity is anomalous we need to compare real-time entropy values with baseline values, and while the analysis of entropy in packet data is not particularly new, to the best of our knowledge there are no published baselines for payload entropy across common network services. We offer two contributions: 1) We analyse several large packet datasets to establish baseline payload information entropy values for common network services, 2) We describe an efficient method for engineering entropy metrics when performing flow recovery from live or offline packet data, which can be expressed within feature subsets for subsequent analysis and machine learning applications.
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    Minimal Optimal Region Generation for Enhanced Object Detection in Aerial Images Using Super-Resolution and Convolutional Neural Networks
    (2023-09-30) García-Aguilar, Ivan; Deka, Lipika; Luque-Baena, Rafael Marcos; Domínguez, Enrique; Lopez-Rubio, Ezequiel
    In recent years, object detection has experienced impressive progress in several fields. However, identifying objects in aerial images remains a complex undertaking due to specific challenges, including the presence of small objects or tightly clustered objects. This paper proposes a novel methodology for enhancing object detection in aerial imagery by combining super-resolution and convolutional neural networks (CNNs). We begin by binarizing the grey zone of the image to detect roads and other regions of interest using the You Only Look Once (YOLO) model. Next, we generate minimal optimal regions. For every one of them, we apply Super-resolution (SR) to improve the number of pixels, generating a new image on which to re-infer. We then apply a CNN to these regions to detect objects more accurately. Our results show that the proposed methodology increases mean average precision in the Unmanned Aerial Vehicle Benchmark Object Detection and Tracking Dataset (UAVDT)
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    Oil spill classification using an autoencoder and hyperspectral technology
    (MDPI, 2024-03-15) Carrasco-Garcia, Maria Gema; Inmaculada Rodríguez-García, M.; Ruiz-Aguilar, Juan Jesus; Deka, Lipika; Elizondo, David; Turias-Domínguez, Ignacio J.
    Hyperspectral technology has been playing a leading role in monitoring oil spills in marine environments, an issue of international concern. In the case of monitoring oil spills in local areas, hyperspectral technology of small dimensions becomes the ideal solution. This research explores the use of encoded hyperspectral signatures to develop automated classifiers capable of discriminating between polluted and clean water, and even distinguishing between various types of oil. The overall objective is to leverage these classifiers to be able to improve the performance of conventional systems that rely solely on hyperspectral imagery. The acquisition of the hyperspectral signatures of water and hydrocarbons was carried out with a spectroradiometer. The range of the spectroradiometer used in this study covers the ranges between [350-1000] (visible near-infrared) and [1000-2500] (short-wavelength infrared). This gives detailed information with regards to the targets of interest. Different neural autoencoders (AEs) have been developed to reduce inputs into different dimensions, from 1 to 15. Each of these encoded sets was used to train decision tree (DT) classifiers. The results are very promising, as they show that AEs performance encoded data with correlation coefficients above 0.95. The classifiers trained with the different sets provide accuracies close to 1.