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.

Hydro-thermal performance of normal-channel facile heat sink using TiO2-H2O mixture (Rutile–Anatase) nanofluids for microprocessor cooling

Abstract: Thermal management of microelectronics is a challenging task in modern high heat generating devices. In this work, thermal performance of normal-channel facile heat sink has been investigated using water and TiO2-H2O (mixture of Rutile and Anatase) nanofluids with volumetric concentration of 0.005% and 0.01%. The maximum reduction in base temperature was noted for TiO2-H2O (∅ = 0.01%) and TiO2-H2O (∅ = 0.005%) as 8.2% and 5.5%, respectively, when compared with water. The thermal performance of normal-channel facile heat sink was then compared with the mini-channel integral fin heat sink. The base temperature of normal-channel facile heat sink was found very close to mini-channel integral fin heat sink with a maximum difference of 1.8%. The total cost to fabricate mini-channel heat sink was almost 5.3 times greater than normal-channel heat sink. So, the normal-channel heat sink has economical advantage over the mini-channel heat sink in terms of lower fabrication cost with similar thermal performance. However, the pressure drop was found greater for normal-channel as compared to mini-channel heat sink. The experimental results of normal-channel facile heat sink were also validated numerically, and a good agreement was found.

Deep Semantic Segmentation and Multi-Class Skin Lesion Classification Based on Convolutional Neural Network

Abstract: Skin cancer is developed due to abnormal cell growth. These cells are grown rapidly and destroy the normal skin cells. However, it’s curable at an initial stage to reduce the patient’s mortality rate. In this article, the method is proposed for localization, segmentation and classification of the skin lesion at an early stage. The proposed method contains three phases. In phase I, different types of the skin lesion are localized using tinyYOLOv2 model in which open neural network (ONNX) and squeeze Net model are used as a backbone. The features are extracted from depthconcat7 layer of squeeze Net and passed as an input to the tinyYOLOv2. The propose model accurately localize the affected part of the skin. In Phase II, 13-layer 3D-semantic segmentation model (01 input, 04 convolutional, 03 batch-normalization, 03 ReLU, softmax and pixel classification) is used for segmentation. In the proposed segmentation model, pixel classification layer is used for computing the overlap region between the segmented and ground truth images. Later in Phase III, extract deep features using ResNet-18 model and optimized features are selected using ant colony optimization (ACO) method. The optimized features vector is passed to the classifiers such as optimized (O)-SVM and O-NB. The proposed method is evaluated on the top MICCAI ISIC challenging 2017, 2018 and 2019 datasets. The proposed method accurately localized, segmented and classified the skin lesion at an early stage.

Data Science Algorithms and Techniques for Smart Healthcare Using IoT and Big Data Analytics

Abstract: Smart healthcare network is an innovative process of synergizing the benefits of sensors, Internet of things (IoT), and big data analytics to deliver improved patient care while reducing the healthcare costs. In recent days, healthcare industry faces vast challenges to save the data generated and to process it in order to extract knowledge out of it. The increasing volume of healthcare data generated through IoT devices, electronic health, mobile health, and telemedicines screening requires the development of new methods and approaches for their handling. In this chapter, we briefly discuss some of the healthcare challenges and big data analytics evolution in this fast-growing area of research with a focus on those addressed to smart health care through remote monitoring. In order to monitor the healthcare conditions of an individual, support from sensor and IoT devices is essential. The objective of this study is to provide healthcare services to the diseased as well as healthy population through remote monitoring using intelligent algorithms, tools, and techniques with faster analysis and expert intervention for better treatment recommendations. The delivery of healthcare services has become fully advanced with integration of technologies. This study proposes a novel smart healthcare big data framework for remotely monitoring physical daily activities of healthy and unhealthy population. The framework is validated through a case study which monitors the physical activities of athletes with sensors placed on wrist, chest, and ankle. The sensors connected to the human body transmit the signals continuously to the receiver. On the other hand, at the receiver end, the signals that are stored and analyzed through big data analytics techniques and machine learning algorithms are used to recognize the activity. Our proposed framework predicts whether the player is active or inactive based on the physical activities. Our proposed model has provided an accuracy of 99.96% which can be adapted to remotely monitor health conditions of old patients in case of Alzheimer’s disease by caregivers, rehabilitation, obesity monitoring, remotely monitoring of sports persons physical exertion, and it can also be beneficial for remotely monitoring chronic diseases which require vital physical information, biological, and genetic data.

Effects of exogenously applied salicylic acid and putrescine alone and in combination with rhizobacteria on the phytoremediation of heavy metals and chickpea growth in sandy soil

Abstract: The present attempt was made to study the role of exogenously applied salicylic acid (SA) and putrescine (Put) on the phytoremediation of heavy metal and on the growth parameters of chickpea grown in sandy soil. The SA and Put were applied alone as well as in combination with plant growth promoting rhizobacteria (PGPR). The PGPRs, isolated from the rhizosphere of chickpea were characterized on the basis of colony morphology and biochemical traits through gram staining, catalase and oxidase tests, and identified by 16S-rRNA gene sequencing as Bacillus subtilis, Bacillus thuringiensis and Bacillus megaterium. The chickpea seeds were soaked in 24 h fresh cultures of isolates for 2–3 h prior to sowing. The growth regulators (PGRs), SA and Put (300 mg/L), were applied to the seedlings as foliar spray at 3-leaf stage. The result revealed that plants treated with SA and Put alone or in combination with PGPRs, significantly enhanced the accumulation of heavy metals in plant shoot. PGPR induces Ni accumula...