In Audio Tagging system we made feature vectors with Mel-frequency cepstral coefficients(MFCC)s and zero cross rating. Apply PCA on feature vectors for dimensionality reduction. A multiclass SVM audio classifier is designed to classify sounds in 41 classes. The results of experiments show that audio classification system classify audio signals with an average accuracy of 72

In Audio Tagging system we made feature vectors with Mel-frequency cepstral coefficients(MFCC)s and zero cross rating. Apply PCA on feature vectors for dimensionality reduction. A multiclass SVM audio classifier is designed to classify sounds in 41 classes. The results of experiments show that audio classification system classify audio signals with an average accuracy of 72

The main objective of our project is to design and implement a system which can recognize Urdu language from audio input. We hope that our Automatic Speech Recognition System (ASR) for Urdu will be an important stepping stone in the development of future systems which will allow the users to interact with them using Urdu speech.

Bitcoin is the first decentralized digital currency. This cryptocurrency was created in 2009 but it became extremely popular in 2017. This project is concerned with predicting the price of Bitcoin using machine learning. The goal is to ascertain with what accuracy can the direction of Bitcoin price in USD can be predicted. The price data is sourced from the Bitcoin Price Index . The task is achieved with varying degrees of success through the implementation of a Long Short Term Memory (LSTM) network

In computing, active data is often cached to shorten data access times, reducelatency and improve input/output. Because almost all application workload is dependent upon I/O operations, caching is used to improve application performance. Various attacks like prime+probe, flush+reload and flush+flushare purposed to steal important information by targeting caches using side channel methods. Flush + flush is stealth in nature and not detectable using available methods. I am designing tool to detect flush+flush.

Domain Adaptation is one of the major problems in Semantic Segmentation where each pixel is labelled as a separate class. A solution is devised in-order to adapt the domain during semantic segmentation. The devised solution tries to adapt the target domain along with source domain using GANs approach. An experiment with fine tuning is also performed. The
overall performance of the system is improved on target domain after adaptation on Cityscapes and WAD dataset respectively

Having precise estimates of when ball bearings should be replaced can help achieve higher efficiency and ensure safety. We used ball bearings vibrations to estimate their remaining useful life (RUL). Features from time, frequency and time-frequency domain were extracted. Their selection was done according to their correlation and monotonicity with time. These were then fused together with LSTM. Double exponential smoothing gave the RUL.

Fault prediction in a complex mechanical system is one of the interesting topics for researchers. The goal of our project is to predict faulty regimes of a train car. The system we considered has accelerometers mounted on several parts and the dataset contains training and testing experiments. With the help of simplified physics based model we deduced 21 subsystems to detect any fault. We implemented Principle Component Analysis (PCA) anomaly detection by extracting data patterns from all training experiments and then each testing experiment has been classified either healthy or unhealthy. In the case of unhealthy experiment, faulty subsystem is detected.

In this project, we have set a gunshot detection system with emphasis on quick response, robustness to external disturbances, and less runtime computations. For the achievement of these objectives we have utilized many comprehensive machine learning techniques, including support vector machines with various kernels, neural networks, convolutional neural networks, autoencoders and anomaly detection. By extracting a large set of features, training and testing on extensive dataset we were able to achieve best results
in training and testing accuracy and in real time as well

Authentication is the process of verifying a persons legitimate right before releasing secure resources. We use keystroke pattern of an individual for the biometric authentication method. The keyboard stroke patterns of a user are continuously monitored, the flight and dwell time for each key are used, and the pattern of these extracted features is checked against the individuals unique typing pattern to provide a continuous risk rating

Clinical decision support algorithms face a lot of reliable implementation challenges in medical diagnosis. A solution is established to treat blindness retinal disease based on deep learning framework. This solution utilizes optical coherence tomography (OCT) images of retinal layers and trains a neural network with scratch. A transfer learning technique has also been applied on image data and data augmentation has also been done. Lastly, significance of segmentation channels with corresponding input images have also been analysed.

In previous practices, Faster R-CNN [1] does object detection with great accuracy using VGG network that generates a large feature map. Oriented response Networks (ORNs) [2], on the other hand requires training of less number of parameters using Active Rotating Filters (ARFs). We aim to exploit ORNs with Faster R-CNN to do object detection on firearms dataset.

There are many OCR available for English. But there are very few for urdu and those which are available uses an old approach of many notations of single character.

Plant fault detection is a primary concern in plant operations and is a key variable in important statistics as productivity, profitability and efficiency. An efficient mechanism for fault detection translates in improvement of key statistics and as such warrants sizeable investments. This project is a PHMsociety data challenge. In this project ML techniques are applied to plant data to assess the start time, end time and type of the fault for plants.

One of the biggest challenge that an e-commerce venture is facing on a daily basis is the order return and cancellation rate. These returns not only cost delivery charges but also the opportunity cost of selling product through a valid order. In this paper, we will use historic online purchase data obtained from one of the biggest e-commerce platform in Pakistan and try to predict products that will be returned or cancelled. These predictions will then allow us to intervene in the product delivery cycle well in advance and avoid the costs associated with returned/cancelled orders.

In this project, we aim to predict unit sales amount of each item in grocery stores based on their historical data, item/store information and oil price etc. For this purpose, we have employed simple MLP (Multi-Layer Perceptron) as baseline and build an LSTM model for accurate sales price forecasting. Our proposed model has a weighted MSE test loss of 0.529 comparing to the 0.85 of baseline

Polysomnographic is a multi-parametric test used in the study of sleep and as a diagnostic tool in sleep medicine. This works aims to provide a multi-level classification technique to automatically identify sleep stages and sleep related disorder in an effort to assist physicians in the diagnosis and treatment of sleep disorder. Four different classifiers including SVM and KNN have been trained using cross validation. Optimal features have been selected using forward feature selection

In this project, we aim to predict unit sales amount of each item in grocery stores based on their historical data, item/store information and oil price etc. For this purpose, we have employed simple MLP (Multi-Layer Perceptron) as baseline and build an LSTM model for accurate sales price forecasting. Our proposed model has a weighted MSE test loss of 0.529 comparing to the 0.85 of baseline

T20 is the shortest format of cricket and essentially the most exciting one. A T-20 game can tilt towards either side within a span of 3, 4 overs. This makes the outcome prediction of T20 match a real challenging problem. To overcome this challenge, we used team and player level statistics coupled with the score and wickets information at different intervals in 2nd innings, in our machine learning model. Understandably, best results were achieved on predicting the outcome close to the end of 2nd innings. Logistic Regression and SVM provided best results with overall accuracy of 84

Traditional automatic speech recognition system was composed of acoustic model (AM), pronunciation model (PM), and language model (LM) which rely on laboriously engineered processing pipelines. In contrast end-to-end speech recognition systems do not need hand-designed components. Our approach uses well optimized RNNs that uses GPU to train the model