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

In digital pathology, preliminary step for the development of Computer Aided Diagnostics involves defining and labelling an Object of Interest. Contrary to traditional tiresome method of observation and marking of objects under microscope, digital pathology enables the use of annotation tools to capture localization information and morphology for targeted object(s). However, it is quite difficult to capture this information due to high inter and intra class variance. In this paper, we have addressed these two different problems by developing an easy to use and efficient tool "Lymphocyte Annotator" that allows a user to draw bounding box(es) on IHC stained images, and as a result, automatically generates multiple variants of segmentation masks for the targeted object. Lymphocyte Annotator provides multiple options for bounding box selection, with variation in bounding box size and type. The developed tool targets labelling of lymphocytes in histopathological images. This annotator generates five different types of masks by using a) Chan Vese Segmentation Model, b) Morphological Active Contours without Edges Model, c) Morphological Geodesic Active Contours Model, d) Adaptive Thresholding and e) Circular Masks, thus providing user with an opportunity to use a mixture of different types of generated masks. Capturing of lymphocytes' morphology using different models incorporates morphological variance and enables training of robust deep Convolutional Neural Network. The annotations created from this tool are by default exported in PASCAL VOC and JSON format. Lymphocyte annotator is an open-source tool, and can be accessed from source, available at https://github.com/m-mohsin-zafar/lysto_labeltool.

Lymphocyte Annotator: CD3 + and CD8 + IHC Stained Patch Image Annotation Tool

Disease diagnostics based on medical imaging is getting popularity day by day. Presence of the atherosclerosis is one of the causes of narrowing of carotid arteries which may block partially or fully blood flow into the brain. Serious brain strokes may occur due to such types of blockages in blood flow. Early detection of the plaque and taking precautionary steps in this regard may prevent from such type of serious strokes. In this paper, we present an automatic image segmentation technique for carotid artery ultrasound images based on active contour approach. In our experimental study, we assume that ultrasound images are properly aligned before applying automatic image segmentation. We have successfully applied the automatic segmentation of carotid artery ultrasound images using snake based model. Qualitative comparison of the proposed approach has been made with the manual initialization of snakes for carotid artery image segmentation. Our proposed approach successfully segments the carotid artery images in an automated way to help radiologists to detect plaque easily. Obtained results show the effectiveness of the proposed approach.

https://www.koreascience.kr:443/article/JAKO201310635656340.pdf

Automatic Carotid Artery Image Segmentation using Snake Based Model

Malware has evolved to pose a major threat to information security. Efficient anti-malware software are essential in safeguarding confidential information from these threats. However, identifying malware continues to be a challenging task. Signature-based detection methods are quick but fail to detect unknown malware. Additionally, traditional machine learning archetype requires a large amount of data to be effective, which hinders the ability of an anti-malware system to quickly learn about new threats with limited training samples. In a real-world setting, the majority of malware are found in the form of Portable Executable (PE) files. While there are various formats of PE files, samples of all formats such as ocx, acm, com, scr, etc., are not readily available in large numbers. Therefore, building a conventional Machine Learning (ML) model with greater generalization for data-scarce PE formats becomes a hefty task. Consequently, in such a scenario, Few-Shot learning (FSL) is helpful to detect the presence of malware, even with a very slight number of training samples. FSL techniques help to make predictions based on an insufficient number of samples. In this paper, we propose a novel architecture based on the Relation Network for FSL implementation. We propose a Discriminative Feature Embedder for feature extraction. These extracted features are passed to our proposed Relation Module (RM) for similarity measure. RM produces the relation scores that lead to improved classification. We use Portable Executable (PE) file formats, i.e., ocx, acm, com and scr, after transforming them into images. We employ five-shot learning and then one-shot learning, which produce 93% accuracy with only one training instance. We observe that the proposed architecture outpace the baseline method and provides enhanced accuracy by up to 94% with only one sample. 

/pdf/detection-of-data-scarce-malware-using-one-shot-learning-14os5ti6.pdf

Detection of Data Scarce Malware Using One-Shot Learning with Relation Network

Objective: To determine the frequency of undiagnosed hypertension (HTN) in patients presenting with stroke.
Study Design: Descriptive cross sectional study.
Place and Duration of Study: Department of medicine, Combined Military Hospital (CMH) Kohat from June 2011 to June 2012.
Material and Methods: A total of 190 patients with stroke were included in the study with non-probability, consecutive sampling. Written informed consent and demographics of patients were obtained and permission of the hospital ethical committee was taken. Detailed past history was obtained especially focusing on whether a known hypertensive patient or using any antihypertensive medication. All the patients were subjected to detailed physical examination. Blood pressure (BP) was measured by using standard mercury sphygmomanometer on two different occasions 10 minutes apart and the patient was labeled as hypertensive if both readings showed equal to or more than 140/90mmHg of blood pressure.
Results: The mean age of patients was 46.31 ± 15.30years. There were 126 (66.3%) males and 64 (33.7%) females. The mean systolic BP of patients was 125.59 ± 19.97mmHg while diastolic BP was 86.32 ± 12.62 mmHg. In this study, there were 64 (33.7%) cases who had undiagnosed HTN before stroke. The difference was insignificant among different age groups and both genders (p-value>0.05).
Conclusion: The frequency of undiagnosed HTN is high in patients presenting with stroke which calls for a proper screening program to diagnose and treat HTN at early stage to prevent such deadly complications.

Frequency of undiagnosed hypertension in patients presenting with stroke

Objective: To determine the common locations of traumatic ulceration in denture supporting tissues following implantation of new complete dentures by prosthodontic postgraduate students.
Methodology: This cross sectional study was conducted in the department of prosthodontics, Khyber College of Dentistry, Peshawar on 184 patients. Patients with newly placed dentures were examined after 3 to 4 days, on their first follow up visit, for the presence of traumatic ulceration. Pre-defined locations in the edentulous jaw were examined for the presence of traumatic ulcers. Data was entered and analyzed with the help of SPSS version 20.
Results: The mean age was 55.85±2.22 years with male to female ratio of 1:1.9. Patients returning with one or more traumatic ulcers 3 to 4 days following the placement of their complete dentures were 141 (76.6%). More traumatic ulcers were found in tissues beneath mandibular dentures (n=,134 72.82%) compared to tissues beneath maxillary dentures (n=105, 57.06%). The most common sites to develop traumatic ulcers were the retromylohyoid area (n=73, 39.7%), maxillary vestibular sulcus between labial frenum and buccal frenum (n=52, 28.3%), maxillary tuberosity (n=51, 27.7%), mandibular vestibular sulcus at buccal shelf region (n=47, 25.5%) and the lingual sulcus at paralingual region (n=36, 19.6%).
Conclusion: Traumatic ulcers in denture bearing tissues were reported in majority of the patients who received complete dentures. Retromylohyoid area was reported as the most prone area to the ulceration than others.

/pdf/common-locations-of-traumatic-ulceration-in-tissues-vmomjavgrz.pdf

Asifullah Khan

Papers

Lymphocyte Annotator: CD3 + and CD8 + IHC Stained Patch Image Annotation Tool

Automatic Carotid Artery Image Segmentation using Snake Based Model

Detection of Data Scarce Malware Using One-Shot Learning with Relation Network

Frequency of undiagnosed hypertension in patients presenting with stroke

Common locations of traumatic ulceration in tissues underneath new conventional complete dentures