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

생체의 모델링과 Simulation

Looking for competent reading sources? We have secrets and lies digital security in a networked world to read, not only check out, yet likewise download them and even check out online. Discover this excellent book writtern by by now, merely here, yeah only here. Get the data in the types of txt, zip, kindle, word, ppt, pdf, as well as rar. Once more, never ever miss to check out online and also download this book in our website right here. Click the link.

Secrets and Lies: Digital Security in a Networked World

RFID (Radio Frequency Identification) systems are one of the most pervasive computing technologies with technical potential and profitable opportunities in a diverse area of applications. Among their advantages is included their low cost and their broad applicability. However, they also present a number of inherent vulnerabilities. This paper develops a structural methodology for risks that RFID networks face by developing a classification of RFID attacks, presenting their important features, and discussing possible countermeasures. The goal of the paper is to categorize the existing weaknesses of RFID communication so that a better understanding of RFID attacks can be achieved and subsequently more efficient and effective algorithms, techniques and procedures to combat these attacks may be developed.

/pdf/classifying-rfid-attacks-and-defenses-5dsj1g8zsz.pdf

Classifying RFID attacks and defenses

Demands of aquatic products are increasing dramatically during past decades. Also quality assurance has gradually received more attention by both producers and consumers. Thus, fish producers are exploring all possible approaches for improving the productivity and profitability. Monitoring of fish state and behaviour during cultivation may help to improve profitability for producers and also reduce the threat of severe loss because of disease and stress incidents. It is necessary to evaluate and measure quality of fish products in accurate, fast and objective way for meeting the different demands of the fish-processing industry after harvesting. Traditional methods are usually time-consuming, expensive, laborious and invasive. Using rapid, inexpensive and noninvasive methods is therefore important and desirable. Optical sensors and machine vision system provide the possibility of developing faster, cheaper and noninvasive methods for in situ and after harvesting monitoring of quality in aquaculture. This review describes the most recent technologies and the suitability of different optical sensors for the fish farming management and also assessment, measurement and prediction of fish products quality. Two major areas of optical sensors applications in aquaculture are discussed in this review: (i) preharvesting and during cultivation; and (ii) post-harvesting. Finally, accuracy and uncertainty of optical sensors applications in aquaculture are discussed. This review showed that MVSs and optical sensors have found real-world application based on tremendous possibility offered by digital camera development and increasing the speed of computer-based processing; however, still new algorithms, methods and re-engineered sensors need to be developed to meet real-world requirements.

Application of machine vision systems in aquaculture with emphasis on fish: state-of-the-art and key issues

Summary Change of tag ownership compromises the security goals of Radio Frequency Identification (RFID). When an attacker clones or steals an authorized subject’s tag, they are willingly granted access as RFID assumes the owner of a tag is always the authorized entity. We present Deckard, a new approach to preventing change of tag ownership. Deckard uses the principles of intrusion detection to look for anomalous behavior which may indicate a change of tag ownership has occurred. We have evaluated its performance in detecting synthesized attacks inside a sanitized RFID proximity tag audit log. The results suggest that intrusion detection systems can be used in RFID, although the weaknesses of statistical anomaly detection are also apparent when used on RFID data. We conclude with a call to further research of intrusion detection in RFID systems.

http://paper.ijcsns.org/07_book/200707/20070712.pdf

Deckard: A System to Detect Change of RFID Tag Ownership

This paper examines the optimisation of the transport scheduling of woodchips for in-field chipping operations whose efficiency depends on a range of factors. It illustrates the advantages of optimising trucking efficiency and cost in the context of the Australian forest industry. The study was enabled using an adapted version of Simulated Annealing and a forestry domain model, and a simulator based on them, called Fast Truck, was implemented for experimental use. Insights arising from the simulator results are discussed in the context of the Australian industry. The factors worth noting are truck payload and chipper utilisation, which by optimisation, account for 52% and 29% of the total cost savings obtained, respectively. These savings arise when better transport control and management occur in chipping operations. Further work will consider ways to implement these optimisations, primarily, by adapting Fast Truck as an optimiser of daily dispatch schedules.

Optimising transport efficiency and costs in Australian wood chipping operations

A taxonomy of system attacker behavior reveals security vulnerabilities in RFID authorization and monitoring systems. RFID systems are classified by their informational goals-typically, authorization and monitoring. Authorization systems replace the more traditional approaches of granting an entity access to a particular zone, whereas monitoring systems establish an entity's location in that zone. Although their informational goals differ, the underlying hardware is identical for both types of systems; consequently, attacks at the hardware level are the same. However, because attacker behavior invalidates each subsystem's informational goals differently, RFID security requirements should consider these goals individually.

An RFID Attacker Behavior Taxonomy

Water quality monitoring using abnormal tail-beat frequency of crucian carp

Forest biomass (FB) is widely used overseas as an industrial energy source, particularly in Europe, but is currently little used in Australia. Typical attributes of FB disadvantage it as a fuel rel...

Potential application of overseas forest biomass supply chain experience to reduce costs in emerging Australian forest biomass supply chains - a literature review.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Luke Mirowski

Author Tools

Chat about Author