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

Machine learning (ML) and Deep learning (DL) models are popular in many areas, from business, medicine, industries, healthcare, transportation, smart cities, and many more. However, the conventional centralized training techniques may not apply to upcoming distributed applications, which require high accuracy and quick response time. It is mainly due to limited storage and performance bottleneck problems on the centralized servers during the execution of various ML and DL-based models. However, federated learning (FL) is a developing approach to training ML models in a collaborative and distributed manner. It allows the full potential exploitation of these models with unlimited data and distributed computing power. In FL, edge computing devices collaborate to train a global model on their private data and computational power without sharing their private data on the network, thereby offering privacy preservation by default. But the distributed nature of FL faces various challenges related to data heterogeneity, client mobility, scalability, and seamless data aggregation. Moreover, the communication channels, clients, and central servers are also vulnerable to attacks which may give various security threats. Thus, a structured vulnerability and risk assessment are needed to deploy FL successfully in real-life scenarios. Furthermore, the scope of FL is expanding in terms of its application areas, with each area facing different threats. In this paper, we analyze various vulnerabilities present in the FL environment and design a literature survey of possible threats from the perspective of different application areas. Also, we review the most recent defensive algorithms and strategies used to guard against security and privacy threats in those areas. For a systematic coverage of the topic, we considered various applications under four main categories: space, air, ground, and underwater communications. We also compared the proposed methodologies regarding the underlying approach, base model, datasets, evaluation matrices, and achievements. Lastly, various approaches' future directions and existing drawbacks are discussed in detail. 

Federated learning for 6G-enabled secure communication systems: a comprehensive survey

AI‐driven network softwarization scheme for efficient message exchange in IoT environment beyond 5G

Amalgamation of Fog Computing and Software Defined Networking in Healthcare 4.0: The Challenges, and a Way Forward.

Credit card (CC) fraud has been a persistent problem and has affected financial organizations. Traditional machine learning (ML) algorithms are ineffective owing to the increased attack space, and techniques such as long short-term memory (LSTM) have shown promising results in detecting CC fraud patterns. However, owing to the black box nature of the LSTM model, the decision-making process could be improved. Thus, in this paper, we propose a scheme, RaKShA, which presents explainable artificial intelligence (XAI) to help understand and interpret the behavior of black box models. XAI is formally used to interpret these black box models; however, we used XAI to extract essential features from the CC fraud dataset, consequently improving the performance of the LSTM model. The XAI was integrated with LSTM to form an explainable LSTM (X-LSTM) model. The proposed approach takes preprocessed data and feeds it to the XAI model, which computes the variable importance plot for the dataset, which simplifies the feature selection. Then, the data are presented to the LSTM model, and the output classification is stored in a smart contract (SC), ensuring no tampering with the results. The final data are stored on the blockchain (BC), which forms trusted and chronological ledger entries. We have considered two open-source CC datasets. We obtain an accuracy of 99.8% with our proposed X-LSTM model over 50 epochs compared to 85% without XAI (simple LSTM model). We present the gas fee requirements, IPFS bandwidth, and the fraud detection contract specification in blockchain metrics. The proposed results indicate the practical viability of our scheme in real-financial CC spending and lending setups.

/pdf/raksha-a-trusted-explainable-lstm-model-to-classify-fraud-70ztrt9z.pdf

RaKShA: A Trusted Explainable LSTM Model to Classify Fraud Patterns on Credit Card Transactions

An annular ring microstrip antenna (ARMSA) with metallic drilled holes and a defected ground structure is proposed for ultra-wideband applications. The annular ring microstrip patch antenna is excited by the microstrip line feed. The outer radius of the annular ring was optimized in this paper to choose the resonant frequency corresponding to the highest mode. The inner radius boosts the structure’s gain. A defected conductive strip with an arc in the center increases bandwidth at the lower end, increases gain and suppresses Co and Cross-pole isolation. Incorporating a metallic via array excites and includes several closely spaced lower order modes, increasing the fractional bandwidth to 108%. The gain of the proposed configuration is 10dBi at the resonance frequency. It offers Front to back lobe ratio (FBR) of 34dB and 97% radiation efficiency. The performance of the fabricated prototype agrees well with the simulated one.

Sudeep Tanwar

Papers

Federated learning for 6G-enabled secure communication systems: a comprehensive survey

AI‐driven network softwarization scheme for efficient message exchange in IoT environment beyond 5G

Amalgamation of Fog Computing and Software Defined Networking in Healthcare 4.0: The Challenges, and a Way Forward.

RaKShA: A Trusted Explainable LSTM Model to Classify Fraud Patterns on Credit Card Transactions

Annular Ring Ultra Wideband Antenna Integrated With Metallic via Array for IoT Applications