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

Data Mining Practical Machine Learning Tools and Techniques

MKT 6009 Marketing Internship (0 semester credit hours) Student gains experience and improves skills through appropriate developmental work assignments in a real business environment. Student must identify and submit specific business learning objectives at the beginning of the semester. The student must demonstrate exposure to the managerial perspective via involvement or observation. At semester end, student prepares an oral or poster presentation, or a written paper reflecting on the work experience. Student performance is evaluated by the work supervisor. Pass/Fail only. Prerequisites: (MAS 6102 or MBA major) and department consent required. (0-0) S MKT 6244 Digital Marketing Strategy (2 semester credit hours) Executive Education Course. The course explores three distinct areas within marketing and sales namely, digital marketing, traditional sales prospecting, and executive sales organization and strategy. The continuing convergence of the digital marketing and sales funnels has created a strategic continuum from digital lead generation to digital sales. The course identifies the current composition of this digital continuum while providing opportunities to evaluate sales and marketing digital strategies. Prerequisites: MKT 6301 and instructor consent required. (2-0) Y MKT 6301 (SYSM 6318) Marketing Management (3 semester credit hours) Overview of marketing management methods, principles and concepts including product, pricing, promotion and distribution decisions as well as segmentation, targeting and positioning. (3-0) S MKT 6309 Marketing Data Analysis and Research (3 semester credit hours) Methods employed in market research and data analysis to understand consumer behavior, customer journeys, and markets so as to enable better decision-making. Topics include understanding different sources of data, survey design, experiments, and sampling plans. The course will cover the techniques used for market sizing estimation and forecasting. In addition, the course will cover the foundational concepts and techniques used in data visualization and \"story-telling\" for clients and management. Corequisites: MKT 6301 and OPRE 6301. (3-0) Y MKT 6310 Consumer Behavior (3 semester credit hours) An exposition of the theoretical perspectives of consumer behavior along with practical marketing implication. Study of psychological, sociological and behavioral findings and frameworks with reference to consumer decision-making. Topics will include the consumer decision-making model, individual determinants of consumer behavior and environmental influences on consumer behavior and their impact on marketing. Prerequisite: MKT 6301. (3-0) Y MKT 6321 Interactive and Digital Marketing (3 semester credit hours) Introduction to the theory and practice of interactive and digital marketing. Topics covered include: online-market research, consumer behavior, conversion metrics, and segmentation considerations; ecommerce, search and display advertising, audiences, search engine marketing, email, mobile, video, social networks, and the Internet of Things. (3-0) T MKT 6322 Internet Business Models (3 semester credit hours) Topics to be covered are: consumer behavior on the Internet, advertising on the Internet, competitive strategies, market research using the Internet, brand management, managing distribution and supply chains, pricing strategies, electronic payment systems, and developing virtual organizations. Further, students learn auction theory, web content design, and clickstream analysis. Prerequisite: MKT 6301. (3-0) Y MKT 6323 Database Marketing (3 semester credit hours) Techniques to analyze, interpret, and utilize marketing databases of customers to identify a firm's best customers, understanding their needs, and targeting communications and promotions to retain such customers. Topics

Marketing for management.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

Genetic programming (GP) is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until high-fitness solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. This unique overview of this exciting technique is written by three of the most active scientists in GP. See www.gp-field-guide.org.uk for more information on the book.

/pdf/a-field-guide-to-genetic-programming-3wkkmsw81n.pdf

A Field Guide to Genetic Programming

We describe the problem of restoring a blurred and noisy image without any prior knowledge of the blurring function and the statistics of additive noise. A multilayer feed-forward neural network based on backpropagation algorithm is used for image restoration. The neural network is trained by applying backpropagation with momentum for fast convergence. The results of the backpropagation neural network model are compared to that of Wiener filter for high, moderate and low signal to noise ratio (SNR) blur functions. Improvement in signal to noise ratio (ISNR) is taken as a performance measure. It is observed that backpropagation neural network learns well in each case and restores all the test images reasonably, while Wiener filter performs well for high and moderate SNR blur but performs poorly for the low SNR case. ISNR values of 5.58 db, 5.15 db and 5.13 db has been achieved with this scheme for the peppers image, in comparison to values of 4.17db, 2.71db and -0.93db using Wiener filter for high, moderate and low SNR blur respectively.

Blind image restoration using multilayer backpropagator

This paper presents a lossless data hiding approach based on integer wavelet transform and variable threshold for a novel application of watermarking. In this novel application, a depth map of an object obtained from sequence of 2D images is secretly embedded in one of the 2-D images for subsequent 3-D analysis after transmission. Additionally, for efficient generation of the depth map, we also propose a new focus measure based on discrete cosine transform (DCT) and principal component analysis. The proposed approach is able not only in extracting the depth map, but also recovers the cover image. Experimental results show the capability of the proposed approach of secretly transmitting and retrieving the depth information. The employment of this novel idea of hiding depth maps in corresponding 2-D cover images could be helpful in medical and military image processing, security based stickers, mobile advertizing, image vision, law enforcement, etc. Additionally, if the depth map is generated through a standard approach, it could also help in the authentication related applications of the cover image.

Variable Threshold Based Reversible Watermarking: Hiding Depth Maps

The catalytic reduction of NO with H2 is of considerable interest due to the pollution effects of NO in air. Previously, computer simulation studies of this reaction system have been made on the basis of a Langmuir–Hinshelwood (thermal) mechanism. We have studied a model through Monte Carlo simulation, which assumes that the reaction can proceed via a non-thermal (precursor) mechanism. When the motion of the precursor is taken into the first nearest neighbourhoods and NO is always adsorbed in a dissociated form, the model predicts a very small steady reactive window, which is limited by two irreversible phase transitions. However, diffusion of N atoms widens the reactive window considerably. When NO is adsorbed as a molecule (leading to dissociated adsorption if a vacancy pair is available), the motion of the precursor in the first neighbourhood does not provide any reactive region. Remarkably, diffusion of N atoms cannot change the situation in this case. However, desorption of NO provides some interesting results.

Catalytic reduction of NO with H2 on a square surface: a Monte Carlo simulation study

Tumor-Infiltrating Lymphocytes are specialized lymphocytes that can detect and kill cancerous cells. Their detection poses many challenges due to significant morphological variations, overlapping occurrence, artifact regions, and the high-class resemblance between clustered areas and artifacts. In this regard, a Lymphocyte Analysis Framework (DC-Lym-AF) based on Deep Convolutional Neural Network (CNN) is proposed to analyze lymphocytes in immunohistochemistry images. The proposed framework comprises: i) pre-processing, ii) screening phase, iii) localization phase, and iv) post-processing. In the screening phase, a custom CNN architecture (Lymph-Dil-Net) is developed to screen lymphocytic regions by performing a patch-level classification. This proposed architecture uses dilated convolutions and shortcut connections to capture multi-level variations and ensure reference-based learning. In contrast, the localization phase utilizes an Attention-guided Multi-Scale Lymphocyte Detector (Attn-MS-LD) to detect lymphocytes. The proposed detector extracts refined and multi-scale features by exploiting dilated convolutions, attention mechanism, and feature pyramid network using its custom attention-aware backbone. The proposed DC-Lym-AF shows exemplary performance on the NuClick dataset compared to existing detection models with an F-score and precision of 0.84 and 0.83, respectively. We verified the generalizability of our proposed framework by participating in a publically open LYON'19 challenge. Results in terms of detection rate (0.76) and F-score (0.73) suggest that the proposed DC-Lym-AF can effectively detect lymphocytes in IHC-stained images collected from different laboratories. In addition, its promising generalization on several datasets implies that it can be turned into a medical diagnostic tool to investigate various histopathological problems. Mini- Abstract A novel Lymphocyte Analysis Framework (DC-Lym-AF) based on Deep Convolutional Neural Network is proposed to analyze lymphocytes in immunohistochemistry images. The proposed framework comprises: i) pre-processing, ii) screening phase, iii) localization phase, and iv) post-processing. The proposed framework outperformed in detecting lymphocytes when compared with state-of-the-art detection models.

Attention guided multi-scale deep object detection framework for lymphocyte analysis in IHC histology images.

In most image enhancement tasks, contrast enhancement is a pre-step for the later processing stages. In this paper, we propose a new technique for adaptive contrast enhancement of compressed images in discrete cosine transform (DCT) domain. The algorithm, at the same time, uses human visual system (HVS) as a guide in avoiding unpleasant block artifacts that often appear at the Hock boundaries as a result of processing in DCT domain. An activity measure determines the level of enhancement that a block can withstand without creating any blocking artifacts and simultaneously enhancing the contrast. Since the enhancement process is done during the decompression stage, the compressibility of the original image is not affected. Subjective and objective tests validate our concept of activity based contrast enhancement and the avoidance of block artifacts in the resultant image.

Asifullah Khan

Papers

Blind image restoration using multilayer backpropagator

Variable Threshold Based Reversible Watermarking: Hiding Depth Maps

Catalytic reduction of NO with H2 on a square surface: a Monte Carlo simulation study

Attention guided multi-scale deep object detection framework for lymphocyte analysis in IHC histology images.

Activity based Contrast Enhancement of Compressed Digital images in DCT Domain