University of Wah

About: University of Wah is a education organization based out in Rawalpindi, Pakistan. It is known for research contribution in the topics: Per capita income & Rhizobacteria. The organization has 258 authors who have published 466 publications receiving 4719 citations.

Efficient hybrid approach to segment and classify exudates for DR prediction

Abstract: Diabetic retinopathy (DR) is initiated due to the severity of diabetes which can finally lead to an incurable blindness. It is a significant reason for optical damage that may cause blindness permanently. There are no main symptoms of DR appearing initially but its quantity and severity rises with the passage of time. Initial screening and diagnosis of DR may help to stop vision loss. Exudates (EXs) are one of the primary clinical symptoms of DR. In this manuscript, a computerized technique is proposed for DR detection based on EXs. The proposed system is consisting of four major steps. The first step is enhancement of region of interest using median filter and adaptive contrast enhancement method. After that, local variance and global threshold methods are utilized for candidate lesions segmentation. Moreover, texture features with multiple classifiers are applied for classification. The proposed method is evaluated in terms of sensitivity, specificity, accuracy and area under curve on DIARETDB1, MESSIDOR and local data sets.

Betalogger: Smartphone Sensor-based Side-channel Attack Detection and Text Inference Using Language Modeling and Dense MultiLayer Neural Network

Abstract: With the recent advancement of smartphone technology in the past few years, smartphone usage has increased on a tremendous scale due to its portability and ability to perform many daily life tasks. As a result, smartphones have become one of the most valuable targets for hackers to perform cyberattacks, since the smartphone can contain individuals’ sensitive data. Smartphones are embedded with highly accurate sensors. This article proposes BetaLogger, an Android-based application that highlights the issue of leaking smartphone users’ privacy using smartphone hardware sensors (accelerometer, magnetometer, and gyroscope). BetaLogger efficiently infers the typed text (long or short) on a smartphone keyboard using Language Modeling and a Dense Multi-layer Neural Network (DMNN). BetaLogger is composed of two major phases: In the first phase, Text Inference Vector is given as input to the DMNN model to predict the target labels comprising the alphabet, and in the second phase, sequence generator module generate the output sequence in the shape of a continuous sentence. The outcomes demonstrate that BetaLogger generates highly accurate short and long sentences, and it effectively enhances the inference rate in comparison with conventional machine learning algorithms and state-of-the-art studies.

Role of PGPR in the Phytoremediation of Heavy Metals and Crop Growth Under Municipal Wastewater Irrigation

Abstract: The use of wastewater for crop growth is a centuries-old practice that gets renewed attention with the rising shortage of freshwater resources in many arid and semiarid regions of the world. Wastewater is extensively used as an inexpensive substitute to conservative irrigation water: supporting livelihoods and generating significant value to the agriculture of urban and peri-urban areas. Many microorganisms are known to inhabit soil, especially rhizosphere, and play an important role in plant development and in remediation of heavy metals. Microbial populations are known to affect heavy metal mobility and availability to the plant through release of chelating agents, acidification, phosphate solubilization, and redox changes. Nanoparticles play a key role in plant growth and development and in the phytoremediation when applied alone or in combination with PGPR. The combined effect of rhizobacteria and Ag nanoparticles in bioremediation has been reviewed in this chapter.