A new framework to alleviate DDoS vulnerabilities in cloud computing

In the communication age, the Internet has growing very fast and most industries rely on it. An essential part of Internet, Web applications like online booking, e-banking, online shopping, and e-learning plays a vital role in everyday life. Enhancements have been made in this domain, in which the web servers depend on cloud location for resources. Many organizations around the world change their operations and data storage from local to cloud platforms for many reasons especially the availability factor. Even though cloud computing is considered a renowned technology, it has many challenges, the most important one is security. One of the major issue in the cloud security is Distributed Denial of Service attack (DDoS), which results in serious loss if the attack is successful and left unnoticed. This paper focuses on preventing and detecting DDoS attacks in distributed and cloud environment. A new framework has been suggested to alleviate the DDoS attack and to provide availability of cloud resources to its users. The framework introduces three screening tests VISUALCOM, IMGCOM, and AD-IMGCOM to prevent the attack and two queues with certain constraints to detect the attack. The result of our framework shows an improvement and better outcomes and provides a recovered from attack detection with high availability rate. Also, the performance of the queuing model has been analysed

Automatic detection of tuberculosis using VGG19 with seagull-algorithm.

Due to various reasons, the incidence rate of communicable diseases in humans is steadily rising, and timely detection and handling will reduce the disease distribution speed. Tuberculosis (TB) is a severe communicable illness caused by the bacterium Mycobacterium-Tuberculosis (M. tuberculosis), which predominantly affects the lungs and causes severe respiratory problems. Due to its significance, several clinical level detections of TB are suggested, including lung diagnosis with chest X-ray images. The proposed work aims to develop an automatic TB detection system to assist the pulmonologist in confirming the severity of the disease, decision-making, and treatment execution. The proposed system employs a pre-trained VGG19 with the following phases: (i) image pre-processing, (ii) mining of deep features, (iii) enhancing the X-ray images with chosen procedures and mining of the handcrafted features, (iv) feature optimization using Seagull-Algorithm and serial concatenation, and (v) binary classification and validation. The classification is executed with 10-fold cross-validation in this work, and the proposed work is investigated using MATLAB® software. The proposed research work was executed using the concatenated deep and handcrafted features, which provided a classification accuracy of 98.6190% with the SVM-Medium Gaussian (SVM-MG) classifier

Rejection of human intestinal allografts: Alone or in combination with the liver

The current results of the present series demonstrate that intestinal allografts are more vulnerable to rejection and continue to be at a significantly higher risk long after transplantation compared with isolated liver allograft recipients. Unexpectedly, a combined liver allograft does not protect small bowel from rejection. The necessarily continuous heavy immunosuppression for these unique recipients is potentially self-defeating. This is clearly demonstrated by their high susceptibility to early and late infectious complications after transplantation as reported in this issue. With the minimal graft-versus-host disease threat in this clinical trial, our revised protocol for future intestinal transplantation is to maximize the passenger leukocyte traffic with supplementary bone marrow from the same intestinal donor in an attempt to augment the development of systemic chimerism and the gradual induction of donor-specific nonreactivity

Optimal Portfolio Management for Engineering Problems Using Nonconvex Cardinality Constraint: A Computing Perspective

The problem of portfolio management relates to the selection of optimal stocks, which results in a maximum return to the investor while minimizing the loss. Traditional approaches usually model the portfolio selection as a convex optimization problem and require the calculation of gradient. Note that gradient-based methods can stuck at local optimum for complex problems and the simplification of portfolio optimization to convex, and further solved using gradient-based methods, is at a high cost of solution accuracy. In this paper, we formulate a nonconvex model for the portfolio selection problem, which considers the transaction cost and cardinality constraint, thus better reflecting the decisive factor affecting the selection of portfolio in the real-world. Additionally, constraints are put into the objective function as penalty terms to enforce the restriction. Note that this reformulated problem cannot be readily solved by traditional methods based on gradient search due to its nonconvexity. Then, we apply the Beetle Antennae Search (BAS), a nature-inspired metaheuristic optimization algorithm capable of efficient global optimization, to solve the problem. We used a large real-world dataset containing historical stock prices to demonstrate the efficiency of the proposed algorithm in practical scenarios. Extensive experimental results are presented to further demonstrate the efficacy and scalability of the BAS algorithm. The comparative results are also performed using Particle Swarm Optimizer (PSO), Genetic Algorithm (GA), Pattern Search (PS), and gradient-based fmincon (interior-point search) as benchmarks. The comparison results show that the BAS algorithm is six times faster in the worst case (25 times in the best case) as compared to the rival algorithms while achieving the same level of performance

Detection of Abiotic Methane in Terrestrial Continental Hydrothermal Systems: Implications for Methane on Mars

The recent detection of methane in the Martian atmosphere and the possibility that its origin could be attributed to biological activity, have highlighted the importance of understanding the mechanisms of methane formation and its usefulness as a biomarker. Much debate has centered on the source of the methane in hydrothermal fluids, whether it is formed biologically by microorganisms, diagenetically through the decomposition of sedimentary organic matter, or inorganically via reduction of CO2 at high temperatures. Ongoing research has now shown that much of the methane present in sea-floor hydrothermal systems is probably formed through inorganic CO2 reduction processes at very high temperatures (greater than 400 C). Experimental results have indicated that methane might form inorganically at temperatures lower still, however these results remain controversial. Currently, methane in continental hydrothermal systems is thought to be formed mainly through the breakdown of sedimentary organic matter and carbon isotope equilibrium between CO2 and CH4 is thought to be rarely present if at all. Based on isotopic measurements of CO2 and CH4 in two continental hydrothermal systems, we suggest that carbon isotope equilibration exists at temperatures as low as 155 C. This would indicate that methane is forming through abiotic CO2 reduction at lower temperatures than previously thought and could bolster arguments for an abiotic origin of the methane detected in the martian atmosphere

Intestinal transplantation at the University of Pittsburgh

Our experience with clinical intestinal transplantation under FK 506 immunosuppression showed that 50% of the recipients were able to be independent from TPN after transplantation, but 10% require partial TPN with functioning grafts, 10% needed total TPN after graft removal, and 30% of the recipients died postoperatively, mostly from sepsis due to severe graft rejection. For further improvement in patient survival and in the quality of life for patients after intestinal transplantation, it is mandatory to establish a new strategy for treatment and prevention of graft rejection and systemic infection

An effective identification of crop diseases using faster region based convolutional neural network and expert systems

The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop

Analysis of Newtonian heating and higher-order chemical reaction on a Maxwell nanofluid in a rotating frame with gyrotactic microorganisms and variable heat source/sink

The goal of this study is to investigate the rotating Maxwell nanoliquid flow incorporating gyrotactic microbes with Newtonian heating and irregular heat source sink. The motion of the flow is induced due to linearly unidirectional elongated surface. The uniqueness of the flow is enhanced by the inclusion of additional phenomenon of higher order chemical reaction incorporated with Darcy Forchheimer flow, Fourier and Fick law. Numerical solution of the formulated problem is developed via bvp4c function in MATLAB. The influence of the embroiled parameters on the flow distribution is demonstrated through various graphs and tables. It is noticed that fluid velocity declines on incrementing the rotation parameter. An upsurge in thermal field is portrayed on augmenting the Newtonian heating. Comparative analysis of the results of the proposed model with previous published research is included which confirms the validity of the current model