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Online transactions are still the backbone of the financial industry worldwide today. Millions of consumers use credit cards for their daily transactions, which has led to an exponential rise in credit card fraud. Over time, many variations and schemes of fraudulent transactions have been reported. Nevertheless, it remains a difficult task to detect credit card fraud in real-time. It can be assumed that each person has a unique transaction pattern that may change over time. The work in this article aims to (1) understand how deep reinforcement learning can play an important role in detecting credit card fraud with changing human patterns, and (2) develop a solution architecture for real-time fraud detection. Our proposed model utilizes the Deep Q network for real-time detection. The Kaggle dataset available online was used to train and test the model. As a result, a validation performance of 97.10% was achieved with the proposed deep learning component. In addition, the reinforcement learning component has a learning rate of 80%. The proposed model was able to learn patterns autonomously based on previous events. It adapts to the pattern changes over time and can take them into account without further manual training. [ABSTRACT FROM AUTHOR]

INTRODUCTION: In image processing noise removal is a hot research field. Lots of studies have been carried out and many algorithms and filters have been planned to improve the image information. There are various noise removal procedures to identify and remove the corrupted pixels. But several image de-noising algorithms apply the filter to the overall image to filter non-corrupted pixels also. To overcome these weaknesses, we proposed an efficient denoising algorithm by cascading Adaptive Median Filter (AMF) with Modified Decision Based Median Filter (MDBMF). OBJECTIVES: To acquire an efficient denoising algorithm for impulse noise reduction by combining AMF and MDBMF methods which are effective, efficient for denoising various kinds of images. To retain the edges, prevent signal deterioration, and ensure non-corrupted image pixels are remaining intact, regardless of various degrees of noise in the image. To avoid the condition where noisy pixels are replaced by other noisy pixels to maintain the quality of images from its degraded noise version such as blur which often takes place during transmission, acquisition, storage, etc. METHODS, RESULTS AND CONCLUSION: The performance corroboration of the proposed efficient denoising algorithmis accomplished employing nine standard grayscale images. The size of all standard images kept 256x256 pixels in this research. The proposed image denoising system has experimented on those images affected with 10% to 90% salt & pepper noise density. Additionally, the performance of the existing state-of-art denoising techniques like AMF, MF, WMF, UMF, and DBMF are contrasted with the proposed hybrid approach. The results showed that de-noised images obtained for 10% to 90% densities levels by proposed hybrid approach are quite better than the quality of denoised images achieved from WMF, UTMF, AMF, and DBMF methods. The proposed algorithm effectively eradicates salt and pepper noise for lower to higher image noise densities levels. [ABSTRACT FROM AUTHOR]

Summary: With the arrival of the Internet of Things (IoT) many devices such as sensors, nowadays can communicate with each other and share data easily. However, the IoT paradigm is prone to security concerns as many attackers try to hit the network and make it vulnerable. In this scenario, security concerns are the most important and to address them various models have been designed to overcome these security issues, but still there exist many emerging variants of botnet attacks such as Mirai, Persirai, and Bashlite that exploits the security breaches. This research article aims to investigate cyber security in the advent of B‐IDS, DDOS, and malware attacks. For this purpose, different machine learning algorithms, namely, support vector machine, naive Bayes, linear regression, artificial neural network, decision tree, random forest, the fuzzy classifier, K‐nearest neighbor, adaptive boosting, gradient boosting, and tree ensemble have been implemented for botnet attack detection. For performance measures, these algorithms have been tested on nine sensor devices over N‐BaIoT datasets to measure the security and accuracy of the intrusion detection system. The results show that the tree‐based algorithm achieved more than 99% accuracy which is quite higher as compared to other tested methods on the same sensor devices. [ABSTRACT FROM AUTHOR]

INTRODUCTION: In these days, the usage of smart-phones and wearable sensors have increased at an exceptional rate. These smart devices are equipped with different sensors such as gyroscope, accelerometer and GPS. By using these sensors to analyze the activity of the end-user, behavioural characteristics of the user can be captured. OBJECTIVES: Although smart-phone and wearable devices provide a platform for conducting social, psychological and physical studies, they still have several limitations and challenges. METHODS: This paper provides a comparative analysis of different classical Machine Learning and Deep Learning algorithms and discusses their accuracy and efficiency for human activity recognition (HAR). RESULTS and CONCLUSION: The paper has primarily used the data captured using wireless sensor devices placed on different parts of a human body, and then compared the results for different classifiers. The conclusion shows that Deep learning schemes are extremely accurate and efficient in comparison with classical machine learning techniques. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the effect of intravenous lidocaine infusion on postoperative pain scores and narcotic consumption in patients undergoing abdominal surgery under general anesthesia. Study Design: Randomized, double blinded controlled trial. Place and Duration: Department of Anesthesia, Combined Military Hospital, Khuzdar from 15th November 2018 to 20th May 2019. Methodology: A total of 80 adult patients were randomly assigned into two groups of 40 each to receive IV lidocaine bolus, 1 mg/kg followed by IV infusion, 2mg/kg/h (group L) or similar volume/kg of saline (group S). Each patient's level of pain was measured with a visual analogue scale (VAS) on arrival in post anesthesia care unit, at 2, 4, 8, 12, and 24 hours after surgery. Total postoperative morphine consumption and the incidence of side effects were also recorded. Results: Intraoperative lidocaine infusion resulted in effective analgesia in first 12 h postoperatively, seen by low pain scores (P=<0.05). The total morphine consumption was also reduced in the lidocaine group (P=<0.05). No signs of systemic lidocaine toxicity were observed. Conclusion: Intraoperative lidocaine infusion should be considered as an adjunct to opioids for postoperative pain management. [ABSTRACT FROM AUTHOR]

This study aims at to know determinants of dividend policy of textile industry with context to Pakistan. All listed textile firms on Karachi stock exchange that paid regular dividends makeup the domain of this work. Lintner's (1956) model is extended using new variables with context to Pakistan. Multiple regression with random effect model is used to test the relationship among study variables. The empirical results suggest that insider ownership, institutional ownership, profitability and dividend paid preceding year are key determinants of dividend policy in textile sector. All estimators are positively and significantly correlated with dividend payout ratio except profitability. Furthermore, the relation between earnings per share (i-e EPS) and dividend payout ratio is insignificant. The empirical results support the evidence that textile industry of Pakistan tend to follow stable dividend policy for they set a target dividend ratio and adjust current payout around set target ratio. [ABSTRACT FROM AUTHOR]

Cluster headache is a relatively uncommon primary headache. The exact aetiology of cluster headache is yet unknown. There are rare case reports of cluster like headache in patients who have had vascular insults, either in the form of a dissection or an ischaemic infarct. The case of a 73 year old man is presented, who had a transient ischaemic stroke with dizziness, vomiting, left leg weakness and non-specific occipital headache that resolved in one day. Two days later, he developed features of partial Wallenberg syndrome which was confirmed on magnetic resonance imaging. One day after the onset of Wallenberg syndrome, he developed typical features of cluster like headache ipsilateral to the stroke, site. The headache was treated with traditional therapy of cluster headache including high flow oxygen and verapamil. The patient responded well to the treatment. This case suggests a possible link of lateral medulla to cluster like headache etiology and further emphasizes that semiology of cluster headache can be secondary to central lesions.