An Approach to Improve the Performance of Web Proxy Cache Replacement Using Machine Learning Techniques

Web caching techniques have been widely used with the objective of caching as many web pages and web objects in the proxy server cache as possible to improve network performance. Web pre-fetching schemes have also been widely discussed where web pages and web objects are pre-fetched into the proxy server cache. This paper presents an approach that integrates web caching and web pre-fetching approach to improve the performance of proxy server’s cache.

In previous research on cache replacement policies, have seen that a block usually becomes dead after some uses. By classifying the dead block and discarding them early, there are various policies have been used but still there are number of problems such as cache pollution, high cache miss rate and cache overhead existed which degrades the system performance. In this paper, a new approach for cache replacement policy has introduced which reduces the cache miss rate and cache overhead by using shared L2 level cache. In terms of less miss rate and good hit rate there is need to deploy effective replacement policies. Cache replacement techniques is one of the crucial design parameter that affects the overall processor performance and also become more important with current growing technological moves in the direction of high associative cache . Keywords: Cache pollution; Cache overhead; Cache miss rate; Cache replacement policy (LRU and LFU); Memory overhead;

In the last few years Artificial Intelligence (AI) algorithms and Machine Learning (ML) approaches have been successfully applied in real-world scenarios like commerce, industry and digital services, but they are not a widespread reality in Software Testing. Due to the complexity of software testing, most of the work of AI/ML applied to it is still academic. This paper briefly presents the state of the art in the field of software testing, applying ML approaches and AI algorithms. The progress analysis of the AI and ML methods used for this purpose during the last three years is based on the Scopus Elsevier, web of Science and Google Scholar databases. Algorithms used in software testing have been grouped by test types. The paper also tries to create relations between the main AI approaches and which type of tests they are applied to, in particular white-box, grey-box and black-box software testing types. We conclude that black-box testing is, by far, the preferred method of software testing, when AI is applied, and all three methods of ML (supervised, unsupervised and reinforcement) are commonly used in black-box testing being the “clustering” technique, Artificial Neural Networks and Genetic Algorithms applied to “fuzzing” and regression testing.

A server side protection from client based DDoS attacks on multilevel proxy. DDoS attacks are continuously sent the threat to the network applications. Such attacks are created by some set of attackers. They create a huge the whole sum of traffic and forces it to the network. Which induces significant injury to the victim server. In a computer network client sent an HTTP request to the server for seeking application resources through a proxy server. The proxy has protected, filter, monitoring the applications against such DDoS attacks. But the client can access the server through different web proxies to seeking application resource. A web server have not at all any technique to identifying malicious client and users of the client. For the reason that considering a proxy to server traffic, proxy conceals the client information and server knows only the information of proxy. Here Hidden semi-Markov Model (HsMM) proposed to describe the time varying traffic behaviors and special behavior of the traffic. An existing system, discovery of attacks is based only the proxy server and client system behavior rather than the actual client user. In such cases, an innocent web proxy or a whole client system may blocked. So this case may affect the many innocent users on the client system. To avoid this problem, a user based approach is employed for finding locality behaviors of the user's system with enhanced http protocol. To add a custom header in the HTTP protocol for detecting actual attacking user of the client. And also proposed a threshold based algorithm (TBAD) with encryption, decryption algorithms for reshaping the suspicious request to normal request. This method can protect the Qos of the legitimate users of client system.

Decision trees are versatile machine learning algorithms that are frequently used in classification and regression tasks. In this work, decision trees are employed as tools for sediment classification using sound waves propagating in the ocean and their behavior and features. We first analyze the structure of received time series at deployed hydrophones, extracting characteristic features (kurtosis and skewness, for example). Feature values then form vectors that are used as input patterns to the trees. A training step is the first stage of the machine learning approach with the trees trained to recognize sediment types based on feature values. The method is subsequently tested on feature vectors obtained from noise-corrupted time series. The performance depends on Signal-to-Noise Ratio values as expected and the method is found to be superior to conventional machine learning approaches. The addition of tools such as principal component analysis as well as spectrogram processing and time-frequency curve fitting further enhances the method. The decision tree technique provides an effective and efficient solution to the problem of sediment classification using acoustic data. [Work supported by ONR.]

Efficient discovery of risk patterns in medical data

In the Wadden Sea, intertidal mussel beds of the blue mussel (Mytilus edulis) and the Pacific oyster (Magallana gigas) form distinct epibenthic communities and local hotspots of high biomass and biodiversity. To detect and evaluate natural and anthropogenic processes, a ground-based monitoring program started over 25 years ago in the German Wadden Sea. In this study, we describe the potential of drones and machine learning approaches for a remote sensing-based integration into an existing monitoring program of intertidal mussel beds. A fixed wing drone was used to cover an area of up to 39ha in a single flight, with an overall time saving potential of 50%. Applying machine learning approaches, a detailed extraction of the intertidal blue mussel bed coverage with an overall accuracy (OA) up to 95.6% was reached, applying a Support Vector Machine (SVM). The application of a multispectral sensor improved the classification performance. Compared to ground-based monitoring, the drone-based method provided significantly more information on the area extension, coverage, and associated algae of the mussel beds. The results show that drones can provide a non-invasive way to survey large and difficult to access areas providing detailed maps of mussel beds and their internal structures.

Market Risk Measurement and the Cattle Feeding Margin: An Application of Value-at-Risk

Access to the internet and WWW is growing extensively, which results in heavy network traffic. To reduce the network traffic proxy server is used. Proxy server reduces the load of server. If the cache replacement algorithm of proxy server's cache is efficient then proxy server will be helpful to reduce the network traffic in more efficient manner. In this paper we are considering proxy server cache to be Level 1 (L1) cache and storage cache of proxy server to be Level 2 (L2) cache. For collecting the real trace CC proxy server is used in an organization. Log of proxy server gives the information of various URLs accessed by various clients with time. For performing experiments various URLs were given a numeric identity. This paper proposes an efficient replacement algorithm on L1 for proxy server. The replacement algorithms taken into consideration are Least Recently Used (LRU), Least Frequently Used (LFU), First In First Out (FIFO). Access Pattern of L1 and L2 are different as if the desired page is not on L1 then it accesses to L2. Thus L1 is having better temporal locality than L2. Thus the replacement algorithm which is giving efficient results for L1 may not be suitable for L2. This paper also proposes a preeminent pair of replacement algorithms for L1 and L2 cache for proxy server.

Recent advances in machine learning (ML) have facilitated its application to a wide range of systems, from complex to quantum. Reservoir computing algorithms have proven particularly effective for studying nonlinear dynamical systems that exhibit collective behaviors, such as synchronizations and chaotic phenomena, some of which still remain unclear. Here, we apply ML approaches to the Kuramoto model to address several intriguing problems, including identifying the transition point and criticality of a hybrid synchronization transition, predicting future chaotic behaviors, and understanding network structures from chaotic patterns. Our proposed method also has further implications, such as inferring the structure of neural networks from electroencephalogram signals. This study, finally, highlights the potential of ML approaches for advancing our understanding of complex systems.

Classification and prediction tasks are common in health research. With the increasing availability of vast health data repositories (e.g. electronic medical record databases) and advances in computing power, traditional statistical approaches are being augmented or replaced with machine learning (ML) approaches to classify and predict health outcomes. ML describes the automated process of identifying ("learning") patterns in data to perform tasks. Developing an ML model includes selecting between many ML models (e.g. decision trees, support vector machines, neural networks); model specifications such as hyperparameter tuning; and evaluation of model performance. This process is conducted repeatedly to find the model and corresponding specifications that optimize some measure of model performance. ML models can make more accurate classifications and predictions than their statistical counterparts and confer greater flexibility when modelling unstructured data or interactions between covariates; however, many ML models require larger sample sizes to achieve good classification or predictive performance and have been criticized as "black box" for their poor transparency and interpretability. ML holds potential in family medicine for risk profiling of patients' disease risk and clinical decision support to present additional information at times of uncertainty or high demand. In the future, ML approaches are positioned to become commonplace in family medicine. As such, it is important to understand the objectives that can be addressed using ML approaches and the associated techniques and limitations. This article provides a brief introduction into the use of ML approaches for classification and prediction tasks in family medicine.

Caching is an object storage mechanism from a web page that has been accessed. With caching, bandwidth usage will be more efficient and the time to access a web page is getting faster. In caching there are 2 techniques used, namely centralized cache and distributed cache. The purpose of this study is to build a distributed cache system that supports Availability, Reliability, and Scalability. This system will overcome various problems found in the centralized cache, such as single point of failure, unreliable, and not scalable. With the availability of Availability, the system will avoid the problem of single point of failure by implementing a failover mechanism. For the Reliability system will still hold (reliable) accept requests from users, because some proxy servers (parent proxy) will serve requests from users. For Scalability, the system can add proxy servers easily and quickly. To develop the system, the developer takes several approaches such as basic learning about proxy servers and related theories. Next is an analysis to build a system that supports Availability, Reliability, and Scalability. Then the analysis phase is implementedand testing of the system is built. Tests carried out are testing centralized cache systems and testing distributed cache systems. The test results will then be compared to see the performance comparison between centralized cache and distributed cache.

BackgroundN-terminal acetylation is one of the most common protein modifications in eukaryotes and occurs co-translationally when the N-terminus of the nascent polypeptide is still attached to the ribosome. This modification has been shown to be involved in a wide range of biological phenomena such as protein half-life regulation, protein-protein and protein-membrane interactions, and protein subcellular localization. Thus, accurately predicting which proteins receive an acetyl group based on their protein sequence is expected to facilitate the functional study of this modification. As the occurrence of N-terminal acetylation strongly depends on the context of protein sequences, attempts to understand the sequence determinants of N-terminal acetylation were conducted initially by simply examining the N-terminal sequences of many acetylated and unacetylated proteins and more recently by machine learning approaches. However, a complete understanding of the sequence determinants of this modification remains to be elucidated.ResultsWe obtained curated N-terminally acetylated and unacetylated sequences from the UniProt database and employed a decision tree algorithm to identify the sequence determinants of N-terminal acetylation for proteins whose initiator methionine (iMet) residues have been removed. The results suggested that the main determinants of N-terminal acetylation are contained within the first five residues following iMet and that the first and second positions are the most important discriminator for the occurrence of this phenomenon. The results also indicated the existence of position-specific preferred and inhibitory residues that determine the occurrence of N-terminal acetylation. The developed predictor software, termed NT-AcPredictor, accurately predicted the N-terminal acetylation, with an overall performance comparable or superior to those of preceding predictors incorporating machine learning algorithms.ConclusionOur machine learning approach based on a decision tree algorithm successfully provided several sequence determinants of N-terminal acetylation for proteins lacking iMet, some of which have not previously been described. Although these sequence determinants remain insufficient to comprehensively predict the occurrence of this modification, indicating that further work on this topic is still required, the developed predictor, NT-AcPredictor, can be used to predict N-terminal acetylation with an accuracy of more than 80%.

For pre-diagnostic surgical process in dental implantology, detection of Inferior Alveolar Nerve Canal (IAC) in dental images is highly essential to avoid surgical complications and injury. In this paper, a feature based machine learning model is developed for the detection of IAC from the mandible regions of dental OPG images. Initially, the soft tissue regions are enhanced by S-CLAHE (Sharpening based Contrast Limited Adaptive Histogram Equalization). Subsequently, Shape features of the IAC using Histogram of Oriented Gradient (HOG) and texture features using Local Forward Rajan Transform (LFRT) are extracted. These features are considered as an input for Machine Learning classifier. From the trained results, the feature points of IAC region are detected by polynomial curve fitting approach. The performance of the classification technique is evaluated with existing machine learning classifiers. Adaboost M2 Ensemble classifier achieves the best accuracy of 96% compared to other state of art techniques such as Naïve Bayes, KNN, SVM, and Decision Tree. Therefore, the proposed method has high potential in IAC detection and avoids complexities in dental implant surgery.

ProWGen: a synthetic workload generation tool for simulation evaluation of web proxy caches