Middleware Design Framework for Mobile Computing

Mobile computing is one of the recent growing fields in the area of wireless networking. The recent standardization efforts accomplished in Web services, with their XML-based formats for registration/discovery, service description, and service access, respectively UDDI, WSDL, and SOAP, certainly represent an interesting first step towards open service composition, which MA supports for mobile computing are expected to integrate within their frameworks soon. A middle-ware that can work even if the network parameters are changed can be a better solution for successful mobile computing. A middle-ware is proposed for handling the entire existing problem in distributed environment. Middleware is about integration and interoperability of applications and services running on heterogeneous computing and communication devices. The services it provides - including identification, authentication, authorization, soft-switching, certification and security - are used in a vast range of global appliances and systems, from smart cards and wireless devices to mobile services and e-Commerce

Middleware Design Framework for Mobile Computing

Abstract. Mobile computing is one of the recent growing fields in the area of wireless networking. The recent standardization efforts accomplished in Web services, with their XML-based formats for registration/discovery, service description, and service access, respectively UDDI, WSDL, and SOAP, certainly represent an interesting first step towards open service composition, which MA supports for mobile computing are expected to integrate within their frameworks soon. A middle-ware that can work even if the network parameters are changed can be a better solution for successful mobile computing. A middle-ware is proposed for handling the entire existing problem in distributed environment. Middleware is about integration and interoperability of applications and services running on heterogeneous computing and communication devices. The services it provides -including identification, authentication, authorization, soft-switching, certification and security -are used in a vast range of global appliances and systems, from smart cards and wireless devices to mobile services and e-Commerce

On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors

Deep learning based medical image classifiers have shown remarkable prowess in various application areas like ophthalmology, dermatology, pathology, and radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD) systems in real clinical setups is severely limited primarily because their decision-making process remains largely obscure. This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists. We used a well-trained and high performing neural network developed by REasoning for COmplex Data (RECOD) Lab for classification of three skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and performed a detailed analysis on its latent space. Two well established and publicly available skin disease datasets, PH2 and derm7pt, are used for experimentation. Human understandable concepts are mapped to RECOD image classification model with the help of Concept Activation Vectors (CAVs), introducing a novel training and significance testing paradigm for CAVs. Our results on an independent evaluation set clearly shows that the classifier learns and encodes human understandable concepts in its latent representation. Additionally, TCAV scores (Testing with CAVs) suggest that the neural network indeed makes use of disease-related concepts in the correct way when making predictions. We anticipate that this work can not only increase confidence of medical practitioners on CAD but also serve as a stepping stone for further development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural Networks (IJCNN) 202

Six Months’ Analysis of Head Injury due to Motor Bike Accidents in Punjab Institute of Neurosciences (PINS), Lahore

Objectives: To analyze the head injury with special emphasis on motor bike accidents, different age groups affected and its impact on society. The study was conducted at the Neurosurgical unit 1 PINS, Lahore, for six month duration.Material and Methods: This is a prospective study. In this study 1600 cases of head injury due to motor bike accidents who presented in thecausality department of Neurosurgical Unit 1 were studied.Results: A total of 1600 cases were included caused by motor bike accidents from May 2018 to October 2018. Most common age group was 12 to 45 years 1056 (66%) of cases, above 45 years 448 (28%) cases, children 0-11 years 96 (6%) cases. In gender distribution 1280 (80%) males and 320 (20%) females. There were only 256 (16%) wearing helmets and 1344 (84%) without helmets. Severity of injury sustained in all cases was: GCS = 13-15 in 1088 (68%) cases; GCS = 9-12 in 368 (23%) cases and GCS = 3-8 in 144 (9%) cases.Conclusion: Head injury due to motor bike accidents is involving the most productive age and increasing the burden on family and health care system. Strict implementation of traffic rules and use of helmets can reduce the incidence

PCA BASED CLASSIFICATION OF SINGLE-LAYERED CLOUD TYPES

The paper presents an automatic classification system, which discriminates the different types of single-layered clouds using Principal Component Analysis (PCA) with enhanced accuracy as compared to other techniques. PCA is an image classification technique, which is typically used for face recognition. PCA can be used to identify the image features called principal components. A principal component is a peculiar feature of an image. The approach described in this paper uses this PCA capability for enhancing the accuracy of cloud image analysis. To demonstrate this enhancement, a software classifier system has been developed that incorporates PCA capability for better discrimination of cloud images. The system is first trained by cloud images. In training phase, system reads major principal features of the different cloud images to produce an image space. In testing phase, a new cloud image can be classified by comparing it with the specified image space using the PCA algorithm

Surgical Outcome of Sellar Suprasellar Brain Tumors through Retracterless Subfrontal Approach

Objectives:  The aim of this study is to see the surgical outcome of Sellar and Suprasellar brain tumors with retractorless modified subfrontal approach. Material and Methods:  We did cohort study of 15 patients who were operated in Neurosurgery Unit 2, PINS. Our study duration is 1 year and follow up duration is of 3 months. Clinical features were related to cranial nerves 2nd, 3rd ,4th, and pituitary gland, dural irritation and temporal lobe compression i.e., diplopia, decrease vision, CSF rhinorrhea, abnormal olfaction, headache, GTCS etc. Results:  In our study, age range was 8 – 62 years with mean age was 35 years. Our 5 patients were male and 10 patients were female. Surgery was performed in all patients through subfrontal approach with retractorless method. In Histopathological report of 2 patients’ findings was Craniopharyngioma, 12 were of pituitary adenoma and 1 was of sellar meningioma. Seven 46.67 percent patients operated successfully with no new neurological deficit. Three 20 percent patients operated but no post op improvement in clinical symptoms, no patients were re-explored postoperatively due to CSF Rhinorrhea. Diabetes Insipidus occurred in 5 (33.3%) patients post-operatively which was managed later on. Conclusion:  Surgery subfrontal approach with retractorless method is the safe corridor for treatment of sellar and Suprasellar brain tumors