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

A reversible watermarking algorithm with very high data-hiding capacity has been developed for color images. The algorithm allows the watermarking process to be reversed, which restores the exact original image. The algorithm hides several bits in the difference expansion of vectors of adjacent pixels. The required general reversible integer transform and the necessary conditions to avoid underflow and overflow are derived for any vector of arbitrary length. Also, the potential payload size that can be embedded into a host image is discussed, and a feedback system for controlling this size is developed. In addition, to maximize the amount of data that can be hidden into an image, the embedding algorithm can be applied recursively across the color components. Simulation results using spatial triplets, spatial quads, cross-color triplets, and cross-color quads are presented and compared with the existing reversible watermarking algorithms. These results indicate that the spatial, quad-based algorithm allows for hiding the largest payload at the highest signal-to-noise ratio.

https://www.digimarc.com/docs/default-source/technology-resources/published-technical-papers/reversible-techniques/dmrc_generalized_integer.pdf?sfvrsn=4

Reversible watermark using the difference expansion of a generalized integer transform

In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.

/pdf/machine-learning-algorithms-real-world-applications-and-3ah2ghoulk.pdf

Machine Learning: Algorithms, Real-World Applications and Research Directions

Determine the value of the variable in each equation.

Basic Algebra = 代数学入門

Neural networks

Association rule mining is a well-researched area where many algorithms have been proposed to improve the speed of mining In this paper, we propose an innovative algorithm called Rapid Association Rule Mining (RARM) to once again break this speed barrier It uses a versatile tree structure known as the Support-Ordered Trie Itemset (SOTrieIT) structure to hold pre-processed transactional data This allows RARM to generate large 1-itemsets and 2-itemsets quickly without scanning the database and without candidate 2-itemset generation It achieves significant speed-ups because the main bottleneck in association rule mining using the Apriori property is the generation of candidate 2-itemsets RARM has been compared with the classical mining algorithm Apriori and it is found that it outperforms Apriori by up to two orders of magnitude (100 times), much more than what recent mining algorithms are able to achieve

Rapid association rule mining

This paper presents new MRF (Markov random field) models in a bidirectional Bayesian framework for accurate motion and occlusion field estimation. With careful selection of the five free parameters required by the models, good experimental results have been obtained. The resultant computational speed is also 5.5 times faster compared with the conventional "iterated conditional mode" relaxation using the proposed fast bidirectional relaxation.

Estimation of occlusion and dense motion fields in a bidirectional Bayesian framework

Some wireless MAC protocols such as IEEE 802.11 were not designed to capitalize on the available multiple channels in an efficient manner. In this paper, we propose a novel multi-channel medium access control protocol for wireless networks which we term us "primary channel assignment based MAC". The protocol is based on the use of primary channel assignment, which is used by other nodes to find the corresponding node. We also introduce a method to reduce the hidden terminal problem encountered in multi-channel designs by proposing a modified transmission range threshold. The scheme eliminates the need for a separate dedicated control channel or time slot that is usually prone to saturation when the traffic increases. The proposed scheme also addresses the broadcast messages problem in multi-channel design. The scheme is based on IEEE 802.11 DCF protocol. The performance of the proposed protocol is compared to two other multi-channel schemes. In an ad-hoc network, the protocol outperforms the other protocols by up to 140%.

Primary channel assignment based MAC (PCAM) - a multi-channel MAC protocol for multi-hop wireless networks

A survey on MAC protocols in OSA networks

Emerging IEEE 802.11 standards provide very high raw bandwidth. However, the overheads introduced by the physical and MAC layers are also increasingly substantial. Moreover, the multi-rate capability provided by the various physical layers requires that the MAC layer adapts the transmission rate according to the channel conditions. Recently, several rate-adaptation algorithms have been proposed. However, all the schemes have considered the rate adaptation for the Data frame only, while assuming that the control frames are always transmitted at a low basic rate. Also, the rate adaptation function is performed either at the sender side or at the receiver side. A new rate-adaptation algorithm called the full auto rate (FAR) algorithm is proposed, which is able to adapt the transmission rate of all the frames (both control and Data). The FAR algorithm combines the sender- and receiver-based methods proposed in the literature, that is, the rate adaptation of the RTS/CTS frames is done at the sending side of these frames while that for the Data/ACK frames is done at the receiving side of the frames. Moreover, in order to cope with the issues involved in virtual carrier sensing (VCS), the authors have proposed a modified virtual carrier sensing (MVCS) mechanism. Both analytical and simulation results show that the FAR algorithm greatly improves the performance of IEEE 802.11.

Amitabha Das

Papers

Rapid association rule mining

Estimation of occlusion and dense motion fields in a bidirectional Bayesian framework

Primary channel assignment based MAC (PCAM) - a multi-channel MAC protocol for multi-hop wireless networks

A survey on MAC protocols in OSA networks

Full auto rate MAC protocol for wireless ad hoc networks