Development of Biofilms for Antimicrobial Resistance

Biofilms are a unit referred to as assemblage of microbial cells growing as surface-attached microbial communities within the natural surroundings. Their genetic and physiological aspects are widely studied. Biofilm development involves the assembly of extracellular compound substances that forms the most bailiwick network. Quorum sensing is one more crucial development specifically connected with biofilm formation in several microorganism species. In ecological purpose, the biofilm offers protection against unfavorable conditions and provides a platform for the genetic transfer. A biofilm-forming bacterium area unit is medically necessary, as they are resistant to several antibiotics and might spread resistant genes. This chapter provides the summary of microorganism biofilm formation and its significance in ecology

Graph-enabled Intelligent Vehicular Network data processing

Intelligent vehicular network (IVN) is the underlying support for the connected vehicles and smart city, but there are several challenges for IVN data processing due to the dynamic structure of the vehicular network. Graph processing, as one of the essential machine learning and big data processing paradigm, which provide a set of big data processing scheme, is well-designed to processing the connected data. In this paper, we discussed the research challenges of IVN data processing and motivated us to address these challenges by using graph processing technologies. We explored the characteristics of the widely used graph algorithms and graph processing frameworks on GPU. Furthermore, we proposed several graph-based optimization technologies for IVN data processing. The experimental results show the graph processing technologies on GPU can archive excellent performance on IVN data

Temporal pattern mining from user generated content

Faster internet, IoT, and social media have reformed the conventional web into a collaborative web resulting in enormous user-generated content. Several studies are focused on such content; however, they mainly focus on textual data, thus undermining the importance of metadata. Considering this gap, we provide a temporal pattern mining framework to model and utilize user-generated content's metadata. First, we scrap 2.1 million tweets from Twitter between Nov-2020 to Sep-2021 about 100 hashtag keywords and present these tweets into 100 User-Tweet-Hashtag (UTH) dynamic graphs. Second, we extract and identify four time-series in three timespans (Day, Hour, and Minute) from UTH dynamic graphs. Lastly, we model these four time-series with three machine learning algorithms to mine temporal patterns with the accuracy of 95.89%, 93.17%, 90.97%, and 93.73%, respectively. We demonstrate that user-generated content's metadata contains valuable information, which helps to understand the users' collective behavior and can be beneficial for business and research. Dataset and codes are publicly available; the link is given in the dataset section

Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model

Recent studies have shown that robust diets recommended to patients by Dietician or an Artificial Intelligent automated medical diet based cloud system can increase longevity, protect against further disease, and improve the overall quality of life. However, medical personnel are yet to fully understand patient-dietician’s rationale of recommender system. This paper proposes a deep learning solution for health base medical dataset that automatically detects which food should be given to which patient base on the disease and other features like age, gender, weight, calories, protein, fat, sodium, fiber, cholesterol. This research framework is focused on implementing both machine and deep learning algorithms like, logistic regression, naive bayes, Recurrent Neural Network (RNN), Multilayer Perceptron (MLP), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM). The medical dataset collected through the internet and hospitals consists of 30 patient’s data with 13 features of different diseases and 1000 products. Product section has 8 features set. The features of these IoMT data were analyzed and further encoded before applying deep and machine and learning-based protocols. The performance of various machine learning and deep learning techniques was carried and the result proves that LSTM technique performs better than other scheme with respect to forecasting accuracy, recall, precision, and $F1$ -measures. We achieved 97.74% accuracy using LSTM deep learning model. Similarly 98% precision, 99% recall and $99\%~F1$ -measure for allowed class is achieved, and for not-allowed class precision is 89%, recall score is 73% and $F1$ Measure score is 80%

Analyzing fibrous tissue pattern in fibrous dysplasia bone images using deep R-CNN networks for segmentation

Predictive health monitoring systems help to detect human health threats in the early stage. Evolving deep learning techniques in medical image analysis results in efficient feedback in quick time. Fibrous dysplasia (FD) is a genetic disorder, triggered by the mutation in Guanine Nucleotide binding protein with alpha stimulatory activities in the human bone genesis. It slowly occupies the bone marrow and converts the bone cell into fibrous tissues. It weakens the bone structure and leads to permanent disability. This paper proposes the study of FD bone image analyzing techniques with deep networks. Also, the linear regression model is annotated for predicting the bone abnormality levels with observed coefficients. Modern image processing begins with various image filters. It describes the edges, shades, texture values of the receptive field. Different types of segmentation and edge detection mechanisms are applied to locate the tumor, lesion, and fibrous tissues in the bone image. Extract the fibrous region in the bone image using the region-based convolutional neural network algorithm. The segmented results are compared with their accuracy metrics. The segmentation loss is reduced by each iteration. The overall loss is 0.24% and the accuracy is 99%, segmenting the masked region produces 98% of accuracy, and building the bounding boxes is 99% of accuracy

Frequency of Autonomic Neuropathy in Type-1 Diabetes Mellitus Patients

Abstract Objectives: To determine the frequency of diabetic autonomic neuropathy in type 1 diabetes mellitus.Methodology: This cross-sectional study was conducted at Medical Unit-IV, Services Institute ofMedical Sciences, Lahore. A total of 236 cases fulfilling the inclusion/exclusion criteria were enrolledfrom Medical OPD Services Institute of Medical Science, Lahore. Informed consent of the patientswas taken to include their data in the study. Detailed history for Diabetes Mellitus was taken. All thepatients were undergoing for evaluation of diabetic autonomic neuropathy. Presence/absence ofDAN was recorded. All this information was recorded.Results: In our study, frequency of diabetic autonomic neuropathy in type 1 diabetes mellitus wasrecorded in 17.80%(n=42) whereas 82.20%(n=194) had no findings of the morbidity.Conclusion: We conclude that the frequency of diabetic autonomic neuropathy in type 1 diabetesmellitus is not very high, but it varies according to diagnostic criteria and population, however,some-other studies in different health centers of our country is required

Helicobacter Pylori in Patients with Hepatitis C Virus

Abstract Objective: To determine the frequency and association of Helicobacter Pylori (H. Pylori) infection among patients infected with hepatitis C virus (HCV). Methodology: The study was conducted in Services Institute of Medical Sciences, Lahore. A total of 200 cases fulfilling the inclusion/exclusion criteria were enrolled from Medical OPD Services Institute of Medical Science, Lahore. An informed consent of the patients was taken to include their data in the study. Detailed history for hepatitis C virus was taken. Blood samples were collected and sent to the hospital laboratory for evaluation of presence/absence of H. Pylori in the subjects of HCV. Results: In this study, out of 200 subjects, 32%(n=64) aged 30-50 years while 68%(n=136) aged 51-80 years, and the mean S.D was calculated as 53.99+8.67 years. 54%(n=108) males and 46%(n=92) females were included. The frequency of H. Pylori in subjects with HCV was recorded in 37.5%(n=75) whereas 62.5%(n=125) had no findings of the morbidity. Conclusion: The frequency of H. Pylori is higher in patients of Hepatitis C. &nbsp

A Console GRID Leveraged Authentication and Key Agreement Mechanism for LTE/SAE

Growing popularity of multimedia applications, pervasive connectivity, higher bandwidth, and euphoric technology penetration among bulk of the human race that happens to be cellular technology users, has fueled the adaptation to long-term evolution (LTE)/system architecture evolution. The LTE fulfills the resource demands of the next generation applications for now. We identify security issues in authentication mechanism used in LTE that without countermeasures might give super user rights to unauthorized users. The LTE uses static LTE key to derive the entire key hierarchy, i.e., LTE follows Evolved Packet System–Authentication and Key Agreement based authentication, which discloses user identity, location, and other personally identifiable information. To counter this, we propose a public key cryptosystem named “International mobile subscriber identity Protected Console Grid based Authentication and Key Agreement (IPG-AKA) protocol” to address the vulnerabilities related to weak key management. From the data obtained from threat modeling and simulation results, we claim that the IPG-AKA scheme not only improves security of authentication procedures, but also shows improvements in authentication loads and reduction in key generation time. The empirical results and qualitative analysis presented in this paper prove that IPG-AKA improves security in authentication procedure and performance in the LTE