Data Mining Practical Machine Learning Tools and Techniques

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

https://www.journal-of-hepatology.eu/article/S0168-8278(15)00259-7/pdf

EASL-ALEH Clinical Practice Guidelines: Non-invasive tests for evaluation of liver disease severity and prognosis.

The latest advances in hepatology were presented in oral and poster presentations. In order to cover the varying subspecialties, the sessions were divided into various sections including 'Acute Liver Failure and Artificial Liver Support', 'Biliary Tract and Immunologic Liver Diseases', 'Cellular and Molecular Biology', 'Clinical and Experimental Hepatobiliary Surgery', 'Hepatotoxicity and Cell Death', 'Transport and Biliary Physiology', 'Viral Hepatitis', 'Evaluation and Treatment of Biliary Disease', 'Necrosis/Apoptosis', 'Portal Hypertension', 'Blood Flow and Vascular Disorders of Cirrhosis', 'Liver Transplantation', 'Fibrogenesis', 'Hepatocellular Carcinoma', 'Metabolism and Genetic Disease', and 'Public Policy, Epidemiology and Decision Analysis'. Drug therapy focused on treatments for viral hepatitis, autoimmune hepatitis, primary biliary cirrhosis and primary sclerosing cholangitis, and recurrent viral disease following liver transplant. High dose interferon therapy or various combinations of interferon/ribavirin (ICN Pharmaceuticals Inc) therapy seem to offer the best current therapy for chronic HCV. PEGylated interferon (F Hoffmann-La Roche Ltd) offers hope for treatment and histologic improvement in patients with chronic HCV. Following liver transplantation, combination interferon/ribavirin therapy may also find success, but caution with new potent immunosuppressant monoclonal antibodies is advised. For HBV, intramuscular H-BIG (NABI) appears to be effective and less costly than iv H-BIG administration following liver transplantation. Percutaneous radiofrequency ablation may hold promise over conventional ethanol injection therapy for small hepatocellular carcinoma. Autoimmune hepatitis may respond to tacrolimus therapy whereas budesonide therapy did not provide any advantage to prednisone therapy. For primary biliary cirrhosis, eicosapentate and ursodeoxycholic acid may provide benefit to some patients while silymarin from milk thistle did not provide any additional benefit. In primary sclerosing cholangitis, high dose ursodeoxycholic acid may provide benefit. Ursodeoxycholic acid may also provide benefit for mothers with intrahepatic cholestasis of pregnancy by decreasing pruritus, lowering laboratory values and allowing deliveries to occur closer to term.

Digestive disease week 2000. American Association for the Study of Liver Diseases

The demand of Public Transport System (PTS) is increasing day by day because it is quite easy, frequently available and cheap in cost as compared to the private transport system. It attracts attentions both from academia and Industry. But at the same time have many issues such as-travel time estimation, route selection, real-time tracking of vehicles, and efficient way of reservation. The solution of these problems helps the drivers as well as to users to use PTS in timely and efficient manner. Although many state-of-the-art proposals exist in the literature to address these issues there is scope available to identify the parameters responsible for an efficient way of real-time PTS reservation. Keeping the above-mentioned points into consideration, this paper provides the detailed taxonomy of PTS and also give proper categorization of the existing approaches. This gives the much-needed clarity with regards to the issues related to Intelligent PTS and their solutions available in the literature. The parameters used for framing the main taxonomy are such as-quality & fare, route selection, seat selection, and seat availability. We also compared all standard algorithm and approaches available for PTS. We also highlight the open issues and challenges encountered by the researchers. These help the beginners who need to begin the work in this.

Comparison and Evaluation of Real Time Reservation Technologies in the Intelligent Public Transport System

The advancement in technology has brought to life the concept of Autonomous vehicles (AV). The primary goal of AV is to reduce driving stress and provide comfort to the occupants. Since AVs can drive themselves, it poses a question of passenger security. Furthermore, AVs are connected to an open network like a public Internet to communicate to the outer world, raising security and privacy concerns. Skillful attackers can effortlessly infiltrate the vehicle by injecting malware which can disrupt the regular operation of the entire AV system. A Deep Learning (DL) and Blockchain framework is proposed for AV to resolve the aforementioned security challenges. The network traffic is continuously monitored, and the malware binaries are converted to grey-scale images, which are then classified by Convolutional Neural Network (CNN) employed in the DL model. The CNN architecture, ResNet50V2, has been tested and proves to be efficient in detecting malware with an accuracy of 97.56%.

Deep Learning and Blockchain-based Framework to Detect Malware in Autonomous Vehicles

In today’s scenario software testing is crucial aspect for any software company because the cost of maintenance is much more than development in software companies. We all make mistakes, some of them are not important, but some of them are harmful for software life cycle. So we should start testing the code/software that we are going to generate from initial stage because at later stage recovery or debugging will not be possible and things cannot always go right –programmer make errors or bugs every moment of the code generation. Since we adopt that we cannot always time right and our work may have error full or bugs, hence we should check our own task every moment. However some errors or bugs come from bad presumption and unsighted fields, so we might make the same errors when we check our own work as we made when we did it. Ideally, we should get someone else to check our task because another person is more likely to smudge the faults. Keeping in mind the above discussion in this paper we provide detailed analysis of testing techniques to generate better test cases. This analysis helps the beginner researchers in this area to select the appropriate technique for testing.

Analysis of Software Testing Techniques: Theory to Practical Approach

Sixth generation (6G) vehicle-to-anything (V2X) networks support intelligent edge computing that leverages data sensing, computation, and offloading among vehicular nodes (VN) with ultra-low latency. Data is heterogeneous with high complex interactions among V2X users and pass via open channels that induce privacy and security concerns. Thus, federated learning (FL) protects user privacy and fine-tunes the learning models at resource-constrained edge nodes to address security and computational concerns at the edge. However, to ensure reliability and trust, we propose a block-chain (BC) and FL-based edge scheme, BFLEdge. It also improves the overall learning rate of the FL model. The proposed scheme consists of three phases, where the first phase uses local machine learning (LML) to model the VN data and store it into the local BC network. The LML block updates are verified in the second phase through a proposed distributed consensus mechanism. Lastly, through 6G communication services, the channel dynamics are modelled as a Markov chain process to reduce end-to-end delay of local BC propagation updates at the edge that improves the V2X system throughput. Simulation and analytical results are proposed based on channel loss, block mining rate, edge latency, and FL-learning rate. The obtained results indicate the viability of the proposed framework against conventional state-of-the-art approaches.

BFLEdge: Blockchain based federated edge learning scheme in V2X underlying 6G communications

The availability of the direct-acting antiviral agents (DAAs) boceprevir and telaprevir provides improved treatment outcomes for many patients infected with hepatitis C virus (HCV) genotype 1. However, HCV infection must first be identified before a decision on treatment can be made and currently many patients remain unaware that they have the virus. Given the lack of prompt diagnosis, disease severity should be determined as a baseline reference for treatment, and novel non-invasive techniques for evaluating fibrosis are now available. For patients receiving a DAA regimen, response-guided therapy based on the detection, absence or level of HCV RNA at specified time points is required to achieve an optimal treatment outcome. Knowledge of the test used to measure HCV RNA and its analytical sensitivity, as well as how to interpret the results correctly, are therefore required to administer therapy appropriately. Furthermore, effective treatment management includes appropriate handling of side effects. This increased complexity associated with DAA regimens has resulted in confusion over many aspects of care, including treatment monitoring, viral load result interpretation and the optimal duration of therapy. These issues are discussed here in addition to the benefits of referring patients infected with HCV to a specialist centre.

Sudeep Tanwar

Papers

Comparison and Evaluation of Real Time Reservation Technologies in the Intelligent Public Transport System

Deep Learning and Blockchain-based Framework to Detect Malware in Autonomous Vehicles

Analysis of Software Testing Techniques: Theory to Practical Approach

BFLEdge: Blockchain based federated edge learning scheme in V2X underlying 6G communications

Complexities of HCV management in the new era of direct-acting antiviral agents.