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Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

International Journal of Computer and Applications

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

Display Omitted DNA microarrays appearances empowered the simultaneous observing of expression levels of a large number of genes.In the proposed methodology, Information gain (IG) is first used for feature selection, then Genetic Algorithm (GA) is employed for feature reduction and finally Genetic Programming (GP) is used for cancer types' classification. A cancers disease in virtually any of its types presents a significant reason behind death surrounding the world. In cancer analysis, classification of varied tumor types is of the greatest importance. Microarray gene expressions datasets investigation has been seemed to provide a successful framework for revising tumor and genetic diseases. Despite the fact that standard machine learning ML strategies have effectively been valuable to realize significant genes and classify category type for new cases, regular limitations of DNA microarray data analysis, for example, the small size of an instance, an incredible feature number, yet reason for limitation its investigative, medical and logical uses. Extending the interpretability of expectation and forecast approaches while holding a great precision would help to analysis genes expression profiles information in DNA microarray dataset all the most reasonable and proficiently. This paper presents a new methodology based on the gene expression profiles to classify human cancer diseases. The proposed methodology combines both Information Gain (IG) and Standard Genetic Algorithm (SGA). It first uses Information Gain for feature selection, then uses Genetic Algorithm (GA) for feature reduction and finally uses Genetic Programming (GP) for cancer types' classification. The suggested system is evaluated by classifying cancer diseases in seven cancer datasets and the results are compared with most latest approaches. The use of proposed system on cancers datasets matching with other machine learning methodologies shows that no classification technique commonly outperforms all the others, however, Genetic Algorithm improve the classification performance of other classifiers generally.

Classification of human cancer diseases by gene expression profiles

Task allocation for maximizing reliability of distributed systems: a simulated annealing approach

A fundamental issue affecting the performance of a parallel application running on a distributed system is the distribution of the workload over the various machines in the system. This problem is known to be NP-hard in most cases and therefore untractable as soon as the number of tasks and/or computers exceeds a few units. This paper first presents a mathematical model for load balancing problem. It then proposes an optimal, memory efficient, two phase algorithm for allocating program modules (tasks) onto processors of a heterogeneous distributed system to minimize the makespan (i.e., the completion time at the maximum loaded processor). The algorithm first finds a near optimal allocation by applying simulated annealing (SA) and then finds an optimal distribution by applying branch-and-bound (BB) technique considering the solution of SA as the initial solution. The proposed algorithm overcomes the low solutions quality that may be obtained by using heuristics. It also overcomes the computational time complexity of the exact algorithms. Some experimental results are given to show the effectiveness of the proposed algorithm.

Two phase algorithm for load balancing in heterogeneous distributed systems

Arabic summarization in Twitter social network

Cloud storages in cloud data centers can be used for enterprises and individuals to store and access their data remotely anywhere anytime without any additional burden. By data outsourcing, users can be relieved from the burden of local data storage and maintenance. However, the major problem of cloud data storage is security. Moreover, cloud users must be able to use the cloud storage just like the local storage, without worrying about the need to verify the data integrity and data consistency. Some researchers have been conducted with the aid of a third party auditor (TPA) to verify the data stored in the cloud and be sure that it is not tampered. However, the TPA is leased by the provider, and after a time, a cloud service provider may contract with the TPA to conceal the loss of data from the user to prevent the defamation. This paper presents a novel secure cloud storage system to ensure the protection of organizations' data from the cloud provider, the third party auditor, and some users who may use their old accounts to access the data stored on the cloud. The proposed system enhances the authentication level of security by using two authentication techniques; time-based one-time password (TOTP) for cloud users verification and automatic blocker protocol (ABP) to fully protect the system from unauthorized third party auditor. The experimental results demonstrate the effectiveness and efficiency of the proposed system when auditing shared data integrity.

/pdf/a-secure-cloud-storage-system-combining-time-based-one-time-4cda2gjx0r.pdf

Gamal Attiya

Papers

Classification of human cancer diseases by gene expression profiles

Task allocation for maximizing reliability of distributed systems: a simulated annealing approach

Two phase algorithm for load balancing in heterogeneous distributed systems

Arabic summarization in Twitter social network

A secure cloud storage system combining time-based one-time password and automatic blocker protocol