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Articles 1 - 30 of 31
Full-Text Articles in Engineering
Embedded Ai For Wheat Yellow Rust Infection Type Classification, Uferah Shafi, Rafia Mumtaz, Muhammad Deedahwar Mazhar Qureshi, Zahid Mahmood, Sikander Khan Tanveer, Ihsan Ul Haq, Syed Mohammad Hassan Zaidi
Embedded Ai For Wheat Yellow Rust Infection Type Classification, Uferah Shafi, Rafia Mumtaz, Muhammad Deedahwar Mazhar Qureshi, Zahid Mahmood, Sikander Khan Tanveer, Ihsan Ul Haq, Syed Mohammad Hassan Zaidi
Articles
Wheat is the most important and dominating crop in Pakistan in terms of production and acreage, which is grown on 37% of the cultivated area, accounting for 70% of the total production. However, wheat yield is highly affected by stripe rust, which is considered the most devastating fungal disease, causing 5.5 million tonnes of loss per year globally. In order to minimize this loss, the accurate and timely detection of rust disease is crucial instead of manual inspection. Towards this end, we propose a system to detect wheat rust disease and classify its infection types into four classes, including healthy, …
Interpreting Energy Utilisation With Shapley Additive Explanations By Defining A Synthetic Data Generator For Plausible Charging Sessions Of Electric Vehicles, Prasant Kumar Mohanty, Gayadhar Panda, Malabika Basu, Diptendu Sinha Roy
Interpreting Energy Utilisation With Shapley Additive Explanations By Defining A Synthetic Data Generator For Plausible Charging Sessions Of Electric Vehicles, Prasant Kumar Mohanty, Gayadhar Panda, Malabika Basu, Diptendu Sinha Roy
Articles
Electric vehicles (EVs) are an effective solution for reducing reliance on non-renewable energy sources. However, the lack of charging infrastructure and concerns over their range are some of the biggest hurdles to adopting EVs. Charging infrastructure for EVs is, however, on the rise. Proper planning of charging stations vis-`a-vis road networks and related points of interest such as transportation hubs, schools, shopping centres, etc., alongside such roads become vital to laying out a plan for such infrastructure, particularly for developing countries like India where EV adoption is relatively in a nascent stage. Synthetic datasets can help overcome these hurdles and …
Schizo-Net: A Novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning On Electroencephalogram-Based Brain Connectivity Indices, Nitin Grover, Aviral Chharia, Rahul Upadhyay, Luca Longo
Schizo-Net: A Novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning On Electroencephalogram-Based Brain Connectivity Indices, Nitin Grover, Aviral Chharia, Rahul Upadhyay, Luca Longo
Articles
Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject’s interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuro-psychiatric patients from healthy subjects. The study presents Schizo-Net , a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain …
Gated Deep Reinforcement Learning With Red Deer Optimization For Medical Image Classification, Narayanan Ganesh, Sambandan Jayalakshmi, Rama Chandran Narayanan, Miroslav Mahdal, Hossam Zawbaa, Ali Wagdy Mohamed
Gated Deep Reinforcement Learning With Red Deer Optimization For Medical Image Classification, Narayanan Ganesh, Sambandan Jayalakshmi, Rama Chandran Narayanan, Miroslav Mahdal, Hossam Zawbaa, Ali Wagdy Mohamed
Articles
The brain is one of the most important and complex organs in the body, consisting of billions of individual cells. Uncontrolled growth and expansion of aberrant cell populations within or around the brain are the main causes of brain tumors. These cells have the potential to harm healthy cells and impair brain function [1]. Tumors can be detected using medical imaging techniques, which are considered the most popular and accurate way to classify different types of cancer, and this procedure is even more crucial as it is noninvasive [2]. Magnetic resonance imaging (MRI) is one such medical imaging technique that …
How Visual Stimuli Evoked P300 Is Transforming The Brain–Computer Interface Landscape: A Prisma Compliant Systematic Review, Jai Kalra, Prashasti Mittal, Nirmiti Mittal, Abhishek Arora, Utkarsh Tewari, Aviral Chharia, Rahul Upadhyay, Vinay Kumar, Luca Longo
How Visual Stimuli Evoked P300 Is Transforming The Brain–Computer Interface Landscape: A Prisma Compliant Systematic Review, Jai Kalra, Prashasti Mittal, Nirmiti Mittal, Abhishek Arora, Utkarsh Tewari, Aviral Chharia, Rahul Upadhyay, Vinay Kumar, Luca Longo
Articles
Non-invasive Visual Stimuli evoked-EEGbased P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics, and education. The current article systematically reviews 147 articles that were published between 2006-2021*. Articles that pass the pre-defined criteria are included in the study. Further, classification based on their primary focus, including article orientation, participants’ age groups, tasks given, databases, the EEG devices used in the studies, classification models, and application domain, is performed. The application-based classification considers …
An Exploration Of Recent Intelligent Image Analysis Techniques For Visual Pavement Surface Condition Assessment., Waqar Shahid Qureshi, Ibrahim Hassan Syed, Susan Mckeever, David Power, Brian Mulry, Kieran Feighan, Dympna O'Sullivan
An Exploration Of Recent Intelligent Image Analysis Techniques For Visual Pavement Surface Condition Assessment., Waqar Shahid Qureshi, Ibrahim Hassan Syed, Susan Mckeever, David Power, Brian Mulry, Kieran Feighan, Dympna O'Sullivan
Articles
Road pavement condition assessment is essential for maintenance, asset management, and budgeting for pavement infrastructure. Countries allocate a substantial annual budget to maintain and improve local, regional, and national highways. Pavement condition is assessed by measuring several pavement characteristics such as roughness, surface skid resistance, pavement strength, deflection, and visual surface distresses. Visual inspection identifies and quantifies surface distresses, and the condition is assessed using standard rating scales. This paper critically analyzes the research trends in the academic literature, professional practices and current commercial solutions for surface condition ratings by civil authorities. We observe that various surface condition rating systems …
Generating Reality-Analogous Datasets For Autonomous Uav Navigation Using Digital Twin Areas, Thomas Lee, Susan Mckeever, Jane Courtney
Generating Reality-Analogous Datasets For Autonomous Uav Navigation Using Digital Twin Areas, Thomas Lee, Susan Mckeever, Jane Courtney
Conference papers
In order for autonomously navigating Unmanned Air Vehicles(UAVs) to be implemented in day-to-day life, proof of safe operation will be necessary for all realistic navigation scenarios. For Deep Learning powered navigation protocols, this requirement is challenging to fulfil as the performance of a network is impacted by how much the test case deviates from data that the network was trained on. Though networks can generalise to manage multiple scenarios in the same task, they require additional data representing those cases which can be costly to gather. In this work, a solution to this data acquisition problem is suggested by way …
Ensemble Approach To The Semantic Segmentation Of Satellite Images, Brendan Kent
Ensemble Approach To The Semantic Segmentation Of Satellite Images, Brendan Kent
Dissertations
Automatic classification and segmentation of land use land cover(LULC) is extremely important for understanding the relationship between humans and nature. Human pressures on the environment have drastically accelerated in the last decades, risking biodiversity and ecosystem services. Remote sensing via satellite imagery is an excellent tool to study LULC. Research has shown that deep learning encoder-decoder architectures have achieved worthy results in the area of LULC, however the application of an ensemble approach has not been well quantified. Studies have shown it to be useful in the area of medical imaging. Ensembling by pooling together predictions to produce better predictions …
Evaluating The Performance Of Vision Transformer Architecture For Deepfake Image Classification, Devesan Govindasamy
Evaluating The Performance Of Vision Transformer Architecture For Deepfake Image Classification, Devesan Govindasamy
Dissertations
Deepfake classification has seen some impressive results lately, with the experimentation of various deep learning methodologies, researchers were able to design some state-of-the art techniques. This study attempts to use an existing technology “Transformers” in the field of Natural Language Processing (NLP) which has been a de-facto standard in text processing for the purposes of Computer Vision. Transformers use a mechanism called “self-attention”, which is different from CNN and LSTM. This study uses a novel technique that considers images as 16x16 words (Dosovitskiy et al., 2021) to train a deep neural network with “self-attention” blocks to detect deepfakes. It creates …
Evaluation Of Automated Eye Blink Artefact Removal Using Stacked Dense Autoencoder, Matthew Rigney
Evaluation Of Automated Eye Blink Artefact Removal Using Stacked Dense Autoencoder, Matthew Rigney
Dissertations
The presence of artefacts in Electroencephalograph (EEG) signals can have a considerable impact on the information they portray. In this comparative study, the automated removal of eye blink artefacts using the constrained latent representation of a stacked dense autoencoders (SDAE) and comparing its ability to that of the manual independent component analysis (ICA) approach was evaluated. A comparative evaluation of 5 stacked dense autoencoder architectures lead to a chosen architecture for which the ability to automatically detect and remove eye blink artefacts were both statistically and humanistically evaluated. The ability of the stacked dense autoencoder was statistically evaluated with the …
Examining The Size Of The Latent Space Of Convolutional Variational Autoencoders Trained With Spectral Topographic Maps Of Eeg Frequency Bands, Taufique Ahmed, Luca Longo
Examining The Size Of The Latent Space Of Convolutional Variational Autoencoders Trained With Spectral Topographic Maps Of Eeg Frequency Bands, Taufique Ahmed, Luca Longo
Articles
Electroencephalography (EEG) is a technique of recording brain electrical potentials using electrodes placed on the scalp [1]. It is well known that EEG signals contain essential information in the frequency, temporal and spatial domains. For example, some studies have converted EEG signals into topographic power head maps to preserve spatial information [2]. Others have produced spectral topographic head maps of different EEG bands to both preserve information in The associate editor coordinating the review of this manuscript and approving it for publication was Ludovico Minati . the spatial domain and take advantage of the information in the frequency domain [3]. …
Modeling Cognitive Load As A Self-Supervised Brain Rate With Electroencephalography And Deep Learning, Luca Longo
Modeling Cognitive Load As A Self-Supervised Brain Rate With Electroencephalography And Deep Learning, Luca Longo
Articles
The principal reason for measuring mental workload is to quantify the cognitive cost of performing tasks to predict human performance. Unfortunately, a method for assessing mental workload that has general applicability does not exist yet. This is due to the abundance of intuitions and several operational definitions from various fields that disagree about the sources or workload, its attributes, the mechanisms to aggregate these into a general model and their impact on human performance. This research built upon these issues and presents a novel method for mental workload modelling from EEG data employing deep learning. This method is self-supervised, employing …
Audio Representations For Deep Learning In Sound Synthesis: A Review, Anastasia Natsiou, Sean O'Leary
Audio Representations For Deep Learning In Sound Synthesis: A Review, Anastasia Natsiou, Sean O'Leary
Articles
The rise of deep learning algorithms has led many researchers to withdraw from using classic signal processing methods for sound generation. Deep learning models have achieved expressive voice synthesis, realistic sound textures, and musical notes from virtual instruments. However, the most suitable deep learning architecture is still under investigation. The choice of architecture is tightly coupled to the audio representations. A sound’s original waveform can be too dense and rich for deep learning models to deal with efficiently - and complexity increases training time and computational cost. Also, it does not represent sound in the manner in which it is …
Flying Free: A Research Overview Of Deep Learning In Drone Navigation Autonomy, Thomas Lee, Susan Mckeever, Jane Courtney
Flying Free: A Research Overview Of Deep Learning In Drone Navigation Autonomy, Thomas Lee, Susan Mckeever, Jane Courtney
Articles
With the rise of Deep Learning approaches in computer vision applications, significant strides have been made towards vehicular autonomy. Research activity in autonomous drone navigation has increased rapidly in the past five years, and drones are moving fast towards the ultimate goal of near-complete autonomy. However, while much work in the area focuses on specific tasks in drone navigation, the contribution to the overall goal of autonomy is often not assessed, and a comprehensive overview is needed. In this work, a taxonomy of drone navigation autonomy is established by mapping the definitions of vehicular autonomy levels, as defined by the …
Adequately Generating Captions For An Image Using Adaptive And Global Attention Mechanisms., Shravan Kumar Talanki Venkatarathanaiahsetty
Adequately Generating Captions For An Image Using Adaptive And Global Attention Mechanisms., Shravan Kumar Talanki Venkatarathanaiahsetty
Dissertations
Generating description to images is a recent surge and with latest developments in the field of Artificial Intelligence, it can be one of the prominent applications to bridge the gap between Computer vision and Natural language processing fields. In terms of the learning curve, Deep learning has become the main backbone in driving many new applications. Image Captioning is one such application where the usage of Deep learning methods enhanced the performance of the captioning accuracy. The introduction of the Encoder-Decoder framework was a breakthrough in Image captioning. But as the sequences got longer the performance of captions was affected. …
Evaluating The Performance Of Transformer Architecture Over Attention Architecture On Image Captioning, Deepti Balasubramaniam
Evaluating The Performance Of Transformer Architecture Over Attention Architecture On Image Captioning, Deepti Balasubramaniam
Dissertations
Over the last few decades computer vision and Natural Language processing has shown tremendous improvement in different tasks such as image captioning, video captioning, machine translation etc using deep learning models. However, there were not much researches related to image captioning based on transformers and how it outperforms other models that were implemented for image captioning. In this study will be designing a simple encoder-decoder model, attention model and transformer model for image captioning using Flickr8K dataset where will be discussing about the hyperparameters of the model, type of pre-trained model used and how long the model has been trained. …
Intelligent Sdn Traffic Classification Using Deep Learning: Deep-Sdn, Ali Malik, Ruairí De Fréin, Mohammed Al-Zeyadi, Javier Andreu-Perez
Intelligent Sdn Traffic Classification Using Deep Learning: Deep-Sdn, Ali Malik, Ruairí De Fréin, Mohammed Al-Zeyadi, Javier Andreu-Perez
Conference papers
Accurate traffic classification is fundamentally important for various network activities such as fine-grained network management and resource utilisation. Port-based approaches, deep packet inspection and machine learning are widely used techniques to classify and analyze network traffic flows. However, over the past several years, the growth of Internet traffic has been explosive due to the greatly increased number of Internet users. Therefore, both port-based and deep packet inspection approaches have become inefficient due to the exponential growth of the Internet applications that incurs high computational cost. The emerging paradigm of software-defined networking has reshaped the network architecture by detaching the control …
Lm-Based Word Embeddings Improve Biomedical Named Entity Recognition: A Detailed Analysis, Liliya Akhtyamova, John Cardiff
Lm-Based Word Embeddings Improve Biomedical Named Entity Recognition: A Detailed Analysis, Liliya Akhtyamova, John Cardiff
Conference Papers
Recent studies have shown that contextualized word embeddings outperform other types of embeddings on a variety of tasks. However, there is little research done to evaluate their effectiveness in the biomedical domain under multi-task settings. We derive the contextualized word embeddings from the Flair framework and apply them to the task of biomedical NER on 5 benchmark datasets, yielding major improvements over the baseline and achieving competitive results over the current best systems. We analyze the sources of these improvements, reporting model performances over different combinations of word embeddings, and fine-tuning and casing modes.
Deep Learning Towards Intelligent Vehicle Fault Diagnosis, Mohammed Al-Zeyadi, Javier Andreu-Perez, Hani Hagras, Chris Royce, Darren Smith, Piotr Rzonsowski, Ali Malik
Deep Learning Towards Intelligent Vehicle Fault Diagnosis, Mohammed Al-Zeyadi, Javier Andreu-Perez, Hani Hagras, Chris Royce, Darren Smith, Piotr Rzonsowski, Ali Malik
Conference papers
Recently, the rapid development of automotive industries has given rise to large multidimensional datasets both in the production sites and after-sale services. Fault diagnostic systems are one of the services that the automotive industries provide. As a consequence of the rapid development of cars features, traditional rule-based diagnostic systems became very limited. Therefore, more sophisticated AI approaches need to be investigated towards more efficient solutions. In this paper, we focus on utilising deep learning so as to build a diagnostic system that is able to estimate the required services in an efficient and effective way. We propose a new model, …
Transformer Neural Networks For Automated Story Generation, Kemal Araz
Transformer Neural Networks For Automated Story Generation, Kemal Araz
Dissertations
Towards the last two-decade Artificial Intelligence (AI) proved its use on tasks such as image recognition, natural language processing, automated driving. As discussed in the Moore’s law the computational power increased rapidly over the few decades (Moore, 1965) and made it possible to use the techniques which were computationally expensive. These techniques include Deep Learning (DL) changed the field of AI and outperformed other models in a lot of fields some of which mentioned above. However, in natural language generation especially for creative tasks that needs the artificial intelligent models to have not only a precise understanding of the given …
Improving Transfer Learning For Use In Multi-Spectral Data, Yuvraj Sharma
Improving Transfer Learning For Use In Multi-Spectral Data, Yuvraj Sharma
Dissertations
Recently Nasa as well as the European Space Agency have made observational satellites images public. The main reason behind opening it to public is to foster research among university students and corporations alike. Sentinel is a program by the European Space Agency which has plans to release a series of seven satellites in lower earth orbit for observing land and sea patterns. Recently huge datasets have been made public by the Sentinel program. Many advancements have been made in the field of computer vision in the last decade. Krizhevsky, Sutskever & Hinton, 2012, revolutionized the field of image analysis by …
Mouldingnet: Deep-Learning For 3d Object Reconstruction, Tobias Burns, Barak Pearlmutter, John B. Mcdonald
Mouldingnet: Deep-Learning For 3d Object Reconstruction, Tobias Burns, Barak Pearlmutter, John B. Mcdonald
Session 2: Deep Learning for Computer Vision
th the rise of deep neural networks a number of approaches for learning over 3D data have gained popularity. In this paper, we take advantage of one of these approaches, bilateral convolutional layers to propose a novel end-to-end deep auto-encoder architecture to efficiently encode and reconstruct 3D point clouds. Bilateral convolutional layers project the input point cloud onto an even tessellation of a hyperplane in the (d Å1)-dimensional space known as the permutohedral lattice and perform convolutions over this representation. In contrast to existing point cloud based learning approaches, this allows us to learn over the underlying geometry of the …
Deep Cnn Frameworks For Comparison For Malaria Diagnosis, Priyadarshini Adyasha Pattanaik, Zelong Wang, Patrick Horain
Deep Cnn Frameworks For Comparison For Malaria Diagnosis, Priyadarshini Adyasha Pattanaik, Zelong Wang, Patrick Horain
Session 2: Deep Learning for Computer Vision
Abstract We compare Deep Convolutional Neural Networks (DCNN) frameworks, namely AlexNet and VGGNet, for the classification of healthy and malaria-infected cells in large, grayscale, low quality and low resolution microscopic images, in the case only a small training set is available. Experimental results deliver promising results on the path to quick, automatic and precise classification in unstrained images.
Place Recognition In Challenging Conditions, Saravanabalagi Ramachandran, John Mcdonald
Place Recognition In Challenging Conditions, Saravanabalagi Ramachandran, John Mcdonald
Session 2: Deep Learning for Computer Vision
Place recognition in a visual SLAM system helps build and maintain a map from multiple traversals of the same environment while closing loops to correct drift accumulated over time. Despite the marked success in visual place recognition research over the past decade, it remains a challenging problem in the context of variations caused due to different times of the day, weather, lighting and seasons. In this paper, we address this problem by progressively training convolutional neural networks in a siamese fashion to generate embeddings that encode semantic and visual features for sequence-aligned image pairs taken at different timescales and viewpoints. …
Deep Convolutional Neural Networks For Estimating Lens Distortion Parameters, Sebastian Lutz, Mark Davey, Aljosa Smolic
Deep Convolutional Neural Networks For Estimating Lens Distortion Parameters, Sebastian Lutz, Mark Davey, Aljosa Smolic
Session 2: Deep Learning for Computer Vision
In this paper we present a convolutional neural network (CNN) to predict multiple lens distortion parameters from a single input image. Unlike other methods, our network is suitable to create high resolution output as it directly estimates the parameters from the image which then can be used to rectify even very high resolution input images. As our method it is fully automatic, it is suitable for both casual creatives and professional artists. Our results show that our network accurately predicts the lens distortion parameters of high resolution images and corrects the distortions satisfactory.
Synthetic Positron Emission Tomography Using Conditional-Generative Adversarial Networks For Healthy Bone Marrow Baseline Image Generation, Patrick Leydon, Martin O'Connell, Derek Greene, Kathleen Curran
Synthetic Positron Emission Tomography Using Conditional-Generative Adversarial Networks For Healthy Bone Marrow Baseline Image Generation, Patrick Leydon, Martin O'Connell, Derek Greene, Kathleen Curran
Session 6: Applications, Architecture and Systems Integration
A Conditional-Generative Adversarial Network has been used for a supervised image-to-image transla- tion task which outputs a synthetic PET scan based on real patient CT data. The network is trained using only data of patients with healthy bone marrow metabolism. This allows for a patient specific synthetic healthy baseline scan to be produced. This can be used by a clinician for comparison to real PET data in the absence of a baseline scan or to aid in the diagnosis of conditions such as Multiple Myeloma which manifest as changes in bone marrow metabolism.
Multi-Sensory Deep Learning Architectures For Slam Dunk Scene Classification, Paul Minogue
Multi-Sensory Deep Learning Architectures For Slam Dunk Scene Classification, Paul Minogue
Dissertations
Basketball teams at all levels of the game invest a considerable amount of time and effort into collecting, segmenting, and analysing footage from their upcoming opponents previous games. This analysis helps teams identify and exploit the potential weaknesses of their opponents and is commonly cited as one of the key elements required to achieve success in the modern game. The growing importance of this type of analysis has prompted research into the application of computer vision and audio classification techniques to help teams classify scoring sequences and key events using game footage. However, this research tends to focus on classifying …
Fisheyemultinet: Real-Time Multi-Task Learning Architecture For Surround-View Automated Parking System., Pullaro Maddu, Wayne Doherty, Ganesh Sistu, Isabelle Leang, Michal Uricar, Sumanth Chennupati, Hazem Rashed, Jonathan Horgan, Ciaran Hughes, Senthil Yogamani
Fisheyemultinet: Real-Time Multi-Task Learning Architecture For Surround-View Automated Parking System., Pullaro Maddu, Wayne Doherty, Ganesh Sistu, Isabelle Leang, Michal Uricar, Sumanth Chennupati, Hazem Rashed, Jonathan Horgan, Ciaran Hughes, Senthil Yogamani
Session 6: Applications, Architecture and Systems Integration
Automated Parking is a low speed manoeuvring scenario which is quite unstructured and complex, requiring full 360° near-field sensing around the vehicle. In this paper, we discuss the design and implementation of an automated parking system from the perspective of camera based deep learning algorithms. We provide a holistic overview of an industrial system covering the embedded system, use cases and the deep learning architecture. We demonstrate a real-time multi-task deep learning network called FisheyeMultiNet, which detects all the necessary objects for parking on a low-power embedded system. FisheyeMultiNet runs at 15 fps for 4 cameras and it has three …
Beef Cattle Instance Segmentation Using Mask R-Convolutional Neural Network, Mohammad Danish
Beef Cattle Instance Segmentation Using Mask R-Convolutional Neural Network, Mohammad Danish
Dissertations
Maintaining the cattle farm along with the wellbeing of every heifer has been the major concern in dairy farm. A robust system is required which can tackle the problem of continuous monitoring of cows. the computer vision techniques provide a new way to understand the challenges related to the identification and welfare of the cows. This paper presents a state-of-art instance segmentation mask RCNN algorithm to train and build a model on a very challenging cow dataset that is captured during the winter season. The dataset poses many challenges such as overlapping of cows, partial occlusion, similarity between cows and …
Adapt At Semeval-2018 Task 9: Skip-Gram Word Embeddings For Unsupervised Hypernym Discovery In Specialised Corpora, Alfredo Maldonado, Filip Klubicka
Adapt At Semeval-2018 Task 9: Skip-Gram Word Embeddings For Unsupervised Hypernym Discovery In Specialised Corpora, Alfredo Maldonado, Filip Klubicka
Other resources
This paper describes a simple but competitive unsupervised system for hypernym discovery. The system uses skip-gram word embeddings with negative sampling, trained on specialised corpora. Candidate hypernyms for an input word are predicted based on cosine similar- ity scores. Two sets of word embedding mod- els were trained separately on two specialised corpora: a medical corpus and a music indus- try corpus. Our system scored highest in the medical domain among the competing unsu- pervised systems but performed poorly on the music industry domain. Our approach does not depend on any external data other than raw specialised corpora.