The main objective of this research is to predict the market performance of Karachi Stock Exchange (KSE) on day closing using different machine learning techniques. The prediction model uses different attributes as an input and predicts market as Positive & Negative. The attributes used in the model includes Oil rates, Gold & Silver rates, Interest rate, Foreign Exchange (FEX) rate, NEWS and social media feed. The old statistical techniques including Simple Moving Average (SMA) and Autoregressive Integrated Moving Average (ARIMA) are also used as input. The machine learning techniques including Single Layer Perceptron (SLP), Multi-Layer Perceptron (MLP), Radial Basis Function (RBF) and Support Vector Machine (SVM) are compared. All these attributes are studied separately also. The algorithm MLP performed best as compared to other techniques. The oil rate attribute was found to be most relevant to market performance. The results suggest that performance of KSE-100 index can be predicted with machine learning techniques.

Subsea pipeline corrosion is considered as a severe problem in offshore oil and gas industry. It directly affects the integrity of the pipeline which further leads to cracks and leakages. At present, subsea visual inspection and monitoring is performed by trained human divers; however, offshore infrastructures are moving from shallow to deep waters due to exhaustion of fossil fuels. Therefore, inhospitable underwater environmental conditions for human diver demand imaging-based robotic solution as an alternate for visual inspection and monitoring of subsea pipelines. However, an unfriendly medium is a challenge for underwater imaging-based inspection and monitoring activities due to absorption and scattering of light that further leads to blur, color attenuation, and low contrast. This paper presents a new method for subsea pipeline corrosion estimation by using color information of corroded pipe. As precursor steps, an image restoration and enhancement algorithm are developed for degraded underwater images. The developed algorithm minimizes blurring effects and enhances color and contrast of the images. The enhanced colors in the imaging data help in corrosion estimation process. The image restoration and enhancement algorithm are tested on both experimentally collected as well as publicly available hazy underwater images. A reasonable accuracy is achieved in corrosion estimation that helped to distinguish between corroded and non-corroded surface areas of corroded pipes. The qualitative and quantitative analyses show promising results that encourage to integrate the proposed method into a robotic system that can be used for real-time underwater pipeline corrosion inspection activity.

Automatic License Plate Detection (ALPD) is an integral component of using computer vision approaches in Intelligent Transportation Systems (ITS). An accurate detection of vehicles’ license plates in images is a critical step that has a substantial impact on any ALPD system’s recognition rate. In this paper, we develop an efficient license plate detecting technique through the intelligent combination of Faster R-CNN along with digital image processing techniques. The proposed algorithm initially detects vehicle(s) in the input image through Faster R-CNN. Later, the located vehicle is analyzed by a robust License Plate Localization Module (LPLM). The LPLM module primarily uses color segmentation and processes the HSV image to detect the license plate in the input image. Moreover, the LPLM module employs morphological filtering and dimension analysis to find the license plate. Detailed trials on challenging PKU datasets demonstrate that the proposed method outperforms few recently developed methods by producing high license plates detection accuracy in much less execution time. The proposed work demonstrates a great feasibility for security and target detection applications.

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have temporal and spatial characteristics that may complement each other and, therefore, pose an intriguing approach for brain-computer interaction (BCI). In this work, the relationship between the hemodynamic response and brain oscillation activity was investigated using the concurrent recording of fNIRS and EEG during ankle joint movements. Twenty subjects participated in this experiment. The EEG was recorded using 20 electrodes and hemodynamic responses were recorded using 32 optodes positioned over the motor cortex areas. The event-related desynchronization (ERD) feature was extracted from the EEG signal in the alpha band (8–11) Hz, and the concentration change of the oxy-hemoglobin (oxyHb) was evaluated from the hemodynamics response. During the motor execution of the ankle joint movements, a decrease in the alpha (8–11) Hz amplitude (desynchronization) was found to be correlated with an increase of the oxyHb (r = −0.64061, p < 0.00001) observed on the Cz electrode and the average of the fNIRS channels (ch28, ch25, ch32, ch35) close to the foot area representation. Then, the correlated channels in both modalities were used for ankle joint movement classification. The result demonstrates that the integrated modality based on the correlated channels provides a substantial enhancement in ankle joint classification accuracy of 93.01 ± 5.60% (p < 0.01) compared with single modality. These results highlight the potential of the bimodal fNIR–EEG approach for the development of future BCI for lower limb rehabilitation.

The purpose of this paper is to implement association rule mining algorithm using Nvidia CUDA framework for general purpose computing on GPU. The major objective is to perform performance comparison of association rule mining algorithm using C based implementation on Intel Quad Core/Core2 Duo CPU with CUDA based implementation on Nvidia G80 and GTX 200 series GPU. The final outcome of this research is the bitmap based association rule mining algorithm and its performance statistics on CPU and GPU.

An explicit content detection (ECD) system to detect Not Suitable For Work (NSFW) media (i.e., image/ video) content is proposed. The proposed ECD system is based on residual network (i.e., deep learning model) which returns a probability to indicate the explicitness in media content. The value is further compared with a defined threshold to decide whether the content is explicit or nonexplicit. The proposed system not only differentiates between explicit/nonexplicit contents but also indicates the degree of explicitness in any media content, i.e., high, medium, or low. In addition, the system also identifies the media files with tampered extension and label them as suspicious. The experimental result shows that the proposed model provides an accuracy of ~ 95% when tested on our image and video datasets.