Machine Learning

An ecomimetic method is developed as an innovative and transdisciplinary design approach rooted in the field of biomimetics. This new method emulates the interrelated complexity of the parts of an ecosystem with the intent to design buildings that are more efficient, effective and holistic. Ecomimetics refers to the design of buildings that mimic ecosystem processes and functions. This approach provides potential opportunities for climate change adaptation and mitigation by optimizing the use of resources in buildings. One challenge to the application of ecomimetics in architecture is the lack of systematic methods supported by scientific research, which may prevent development in this field. A theoretical basis and the initial development of an ecomimetic design method is presented, with a description of each step of the design process. Ecological systems are selected for functional properties that match architectural design goals, and then design tools are used to abstract and transfer those properties to architectural systems. The design tools integrated in the method are from the fields of ecological engineering, systems dynamics and architecture. The case of the Eastgate Center in Harare, Zimbabwe, is used to illustrate the method.

Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible, wide and deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two. We also improve the state-of-the-art on a plethora of common image classification benchmarks.

Web textual advertising can be interpreted as a search problem over the corpus of ads available for display in a particular context. In contrast to conventional information retrieval systems, which always return results if the corpus contains any documents lexically related to the query, in Web advertising it is acceptable, and occasionally even desirable, not to show any results. When no ads are relevant to the user's interests, then showing irrelevant ads should be avoided since they annoy the user and produce no economic benefit. In this paper we pose a decision problem "whether to swing", that is, whether or not to show any of the ads for the incoming request. We propose two methods for addressing this problem, a simple thresholding approach and a machine learning approach, which collectively analyzes the set of candidate ads augmented with external knowledge. Our experimental evaluation, based on over 28,000 editorial judgments, shows that we are able to predict, with high accuracy, when to "swing" for both content match and sponsored search advertising.

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This paper presents a novel approach and a method of learning analytics to study student agency in higher education. Agency is a concept that holistically depicts important constituents of intentional, purposeful, and meaningful learning. Within workplace learning research, agency is seen at the core of expertise. However, in the higher education field, agency is an empirically less studied phenomenon with also lacking coherent conceptual base. Furthermore, tools for students and teachers need to be developed to support learners in their agency construction. We study student agency as a multidimensional phenomenon centring on student-experienced resources of their agency. We call the analytics process developed here student agency analytics, referring to the application of learning analytics methods for data on student agency collected using a validated instrument. The data are analysed with unsupervised and supervised methods. The whole analytics process will be automated using microservice architecture. We provide empirical characterisations of student-perceived agency resources by applying the analytics process in two university courses. Finally, we discuss the possibilities of using agency analytics in supporting students to recognise their resources for agentic learning and consider contributions of agency analytics to improve academic advising and teachers' pedagogical knowledge.

Human activity recognition based on sensor data is a topic with great potential for customized healthcare. Here, an end-to-end deep learning architecture for human activity/transition recognition is developed, achieving an error rate of 0.82%. Various deep learning models are analyzed, and a hyperparameter search is conducted to optimize our chosen model. First, an LSTM architecture is examined, which has the advantage of allowing variable-length input sequences for both training and inference. However, our best architecture (HATRNet) is a deep convolutional neural network with late sensor fusion i.e. separate processing pipelines for subsets of the input channels. We feed in zero-padded time sequences to our network, and achieve accuracy exceeding the state-of-the-art reported in literature--all without the use of hand-extracted features.

Object-based image analysis (OBIA) is a method of assessing remote sensing data that uses morphometric and spectral parameters simultaneously to identify features in remote sensing imagery. Over the past 10-15 years, OBIA methods have been introduced to detect archaeological features. Improvements in accuracy have been attained by using a greater number of morphometric variables and multiple scales of analysis. This article highlights the developments that have occurred in the application of OBIA within archaeology and argues that OBIA is both a useful and necessary tool for archaeological research. Additionally, I discuss future research paths using this method. Some of the suggestions put forth here include: pushing for multifaceted research designs utilizing OBIA and manual interpretation, using OBIA methods for directly studying landscape settlement patterns, and increasing data sharing of methods between researchers.

Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis. The relevance of these techniques is increasing as current sensory devices are developed with ever higher resolution, and problems involving multimodal data sources become more common. A plethora of feature extraction methods are available in the literature collectively grouped under the field of Multivariate Analysis (MVA). This paper provides a uniform treatment of several methods: Principal Component Analysis (PCA), Partial Least Squares (PLS), Canonical Correlation Analysis (CCA) and Orthonormalized PLS (OPLS), as well as their non-linear extensions derived by means of the theory of reproducing kernel Hilbert spaces. We also review their connections to other methods for classification and statistical dependence estimation, and introduce some recent developments to deal with the extreme cases of large-scale and low-sized problems. To illustrate the wide applicability of these methods in both classification and regression problems, we analyze their performance in a benchmark of publicly available data sets, and pay special attention to specific real applications involving audio processing for music genre prediction and hyperspectral satellite images for Earth and climate monitoring.

Today, it's impossible to deploy effective cyber security technology without relying heavily on advanced techniques like machine learning and deep learning. Cyber security is a growing challenge in the era of Internet. Filling an important gap between machine learning and cyber security communities, it discusses topics covering a wide range of modern and practical machine learning techniques, frameworks and development tools to enable readers to engage with the cutting-edge research across various aspects of cyber security.

Waikato Environment for Knowledge Analysis (WEKA) is a comprehensive suite of Java class libraries that implement many state-of-the-art machine learning/data mining algorithms. Non-programmers interact with the software via a user interface component called the Knowledge Explorer.

I the undersigned do hereby endorse that the information contained herein is my original work under the management of my supervisor. Where necessary, citations have been added to acknowledge other researchers whose work I have referred to. Name Reg No.

This paper investigates the accuracy and effectiveness of the face detection and recognition algorithms using OpenCV and C# computer language. The adaboost algorithm [2] is used for face detection and PCA algorithm[1] is used for face recognition. This paper also investigates the robustness of the face recognition system when an unknown person is being detected, wherein the system will send an email to the owner of the system using SMTP [7]. The door lock can also be accessed remotely from any part of the world by using a Dropbox [8] account.

One aspect of a growing VR industry that developers have to face is the ethics behind the technology. This includes making sure that technology is readily available and accessible to as large of a population as possible. Current consumer virtual reality (VR) headsets typically utilize two controllers to navigate a virtual environment, leading to accessibility issues for potential users that cannot effectively operate a controller. We propose NaVRgate, a proof of concept idea that removes the need for controllers in which a user uses expressions to navigate a virtual environment. The system utilizes the computer webcam and computer vision face and eye position tracking to capture the nature of expression tracking, with certain positional thresholds representing different facial expressions. To test this system, We design a game environment where a user navigates with either a controller or the face position tracker, collecting a set of orbs scattered around the map as quickly as they can, comparing the efficiency between navigation through the novel computer vision and traditional controller methods. Users are also questioned on the difficulty of use and experience with each control input method. This paper details the process of the development and drafts, to the statistical experiment constructed to determine the efficiency of head gestures.

In this study, the researcher has advocated the importance of human intelligence in language learning since software or any Learning Management System (LMS) cannot be programmed to understand the human context as well as all the linguistic structures contextually. This study examined the extent to which language learning is perilous to machine learning and its programs such as Artificial Intelligence (AI), Pattern Recognition, and Image Analysis used in much assistive learning techniques such as voice detection, face detection and recognition, personalized assistants, besides language learning programs. The researcher argues that language learning is closely associated with human intelligence, human neural networks and no computers or software can claim to replace or replicate those functions of human brain. This study thus posed a challenge to natural language processing (NLP) techniques that claimed having taught a computer how to understand the way humans learn, to understand text without any clue or calculation, to realize the ambiguity in human languages in terms of the juxtaposition between the context and the meaning, and also to automate the language learning process between computers and humans. The study cites evidence of deficiencies in such machine learning software and gadgets to prove that in spite of all technological advancements there remain areas of human brain and human intelligence where a computer or its software cannot enter. These deficiencies highlight the limitations of AI and super intelligence systems of machines to prove that human intelligence would always remain superior.

With the increasing utilization of the Internet and its provided services, an increase in cyber-attacks to exploit the information occurs. A technology to store and maintain user's information that is mostly used for its simplicity and low-cost services is cloud computing (CC). Also, a new model of computing that is noteworthy today is mobile cloud computing (MCC) that is used to reduce the limitations of mobile devices by allowing them to offload certain computations to the remote cloud. The cloud environment may consist of critical or essential information of an organization; therefore, to prevent this environment from possible attacks a security solution is needed. An intrusion detection system (IDS) is a solution to these security issues. An IDS is a hardware or software device that can examine all inside and outside network activities and recognize doubtful patterns that may demonstrate a network attack and automatically alert the network (or system) administrator. Because of the ability of an IDS to detect known/unknown (inside/outside) attacks, it is an excellent choice for securing cloud computing. Various methods are used in an intrusion detection system to recognize attacks more accurately. Unlike survey papers presented so far, this paper aims to present a comprehensive survey of intrusion detection systems that use computational intelligence (CI) methods in a (mobile) cloud environment. We firstly provide an overview of CC and MCC paradigms and service models, also reviewing security threats in these contexts. Previous literature is critically surveyed, highlighting the advantages and limitations of previous work. Then we define a taxonomy for IDS and classify CI-based techniques into single and hybrid methods. Finally, we highlight open issues and future directions for research on this topic.

Plants are fundamentally important to life. Key research areas in plant science include plant species identification, weed classification using hyper spectral images, monitoring plant health and tracing leaf growth, and the semantic interpretation of leaf information. Botanists easily identify plant species by discriminating between the shape of the leaf, tip, base, leaf margin and leaf vein, as well as the texture of the leaf and the arrangement of leaflets of compound leaves. Because of the increasing demand for experts and calls for biodiversity, there is a need for intelligent systems that recognize and characterize leaves so as to scrutinize a particular species, the diseases that affect them, the pattern of leaf growth, and so on. We review several image processing methods in the feature extraction of leaves, given that feature extraction is a crucial technique in computer vision. As computers cannot comprehend images, they are required to be converted into features by individually analyzing image shapes, colors, textures and moments. Images that look the same may deviate in terms of geometric and photometric variations. In our study, we also discuss certain machine learning classifiers for an analysis of different species of leaves.

The number of institutions offering machine learning courses is on the rise. Supplementary materials that help teach these courses fail to address one of the most important steps of the machine learning process, namely identifying a problem, and determining whether it is appropriate for machine learning. We address this problem by first reviewing frameworks in extant work, then proposing a decision flow to help students determine whether an input-output relationship is appropriate for machine learning. Following the discussion of the steps in the decision flow, we present a course assignment that reinforces the concepts in the decision flow. We conclude by discussing the lessons learned after using this assignment in a graduate class.

Although artificial intelligence is the product of science-fiction literature, it currently represents a significant branch of computer science dealing with intelligent behavior, machine learning, and machine adaptation. It has become a discipline that attempts to answer real-world problems. Artificial intelligence systems are nowadays widely used in economics and medicine, design or military. The role of archives is changing worldwide. In this grandiose transformation, archives need to be at the forefront of their own future, so that they can steer, guide, and not lose out.
The vast masses of information in archives provide an excellent platform for the exploitation of artificial intelligence. The mass of data can be a great help not only for research but also for policy preparation and in some areas of public administration in the not too distant future.

7th International Conference on Artificial Intelligence and Applications (AI 2021) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference
looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and
share cutting-edge development in the field. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to.

s Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future research directions in the field.

Objectives: The main objective of research is to identify a valid model that ensures the capabilities of national counterintelligence interests in the processes of identification, analysis and response to hybrid threats in cyberspace associated with the national security strategy taking into account the capabilities provided by advanced artificial intelligence. Based on this strategic approach, provide key factors for roadmap of adoption in the process of introducing disruptive technologies in counter-intelligence processes for maximization of results in technology adoption. Methodology: The applied research methodology is mixed. It will combine qualitative and quantitative criteria according to the proposed objectives by each research phase.

Today it is almost impossible to retrieve information with a keyword search when the information is spread over several pages. The Semantic Web is an extension of the current web in which information is given well-defined meaning. Web personalization is the one application of semantic web usage mining. In this report we have explored comparison of various collaborative filtering techniques. Those techniques are memory based, model based and hybrid collaborative filtering. Our study shows that the performance of hybrid collaborative filtering technique is better than memory based and model based collaborative filtering technique. We have introduced normalization step, which will improve accuracy of traditional collaborative filtering techniques.

Alle Inhalte der Zeitschrift für Praktische Philosophie sind lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz.
Intransparente Diskriminierung durch maschinelles Lernen Intransparent discrimination by machine learning heiner Koch, duisburg-essen Zusammenfassung: Maschinelles Lernen kann zu Diskriminierungen anhand von völlig neuen Merkmalen führen. Diese Merkmale können uns außerdem nicht transparent sein. Um dies zu zeigen, werde ich argumentieren, dass Diskriminierung nicht nur anhand von Merkmalen in abschließenden Listen erfolgen kann, sondern grund-sätzlich anhand beliebiger Merkmale, und dass Algorithmen trotz fehlender men-taler Eigenschaften diskriminierend sein können. Zudem werde ich die Probleme beschreiben, die damit einhergehen, dass diese neuen Formen der Diskriminierung nur schwer erkennbar sind, weil die eingesetzten Algorithmen intransparent sind. Hierbei sind drei Arten der Intransparenz zu unterschieden, die jeweils unterschied-liche Auswirkungen auf die Erkennbarkeit von Diskriminierungen haben: (i) Merkmale, anhand derer ungleich behandelt wird, können unbekannt sein (ii) Merkmale, anhand derer ungleich behandelt wird, können unverständlich sein (sie sind zu komplex oder "chaotisch", um von Menschen sinnvoll erfasst werden zu können) (iii) es können Erklärungen dafür fehlen, wie und weshalb bestimmte Merkmale für eine Ungleichbehandlung herangezogen werden Gerade die Kombination aus Intransparenz und Diskriminierung anhand neuer Merkmale stellt eine besondere Herausforderung für die philosophische Debatte um Diskriminierung dar. Bisherige Ansätze beschränken sich darauf, verständliche Merkmale zu identifizieren oder zu erzeugen, die ihre Träger_innen unter expliziten Diskriminierungsschutz stellen. Damit geraten jedoch neue Diskriminierungen aus dem Blick, die in Zukunft erhebliche Auswirkungen haben können. Um intranspa-rente Diskriminierungen aufzudecken, müssen unbekannte Merkmale identifiziert werden, unverständliche Merkmale hinreichend verständlich gemacht werden und Korrelationen und Trainingsverfahren erklärbar sein. Anschließend müssen die dis-kriminierenden Elemente des Algorithmus beseitigt werden können. Insofern keine angemessenen Lösungsstrategien vorliegen, muss darüber nachgedacht werden, für 266 Intransparente Diskriminierung durch maschinelles Lernen bestimmte Kontexte nur Formen des maschinellen Lernens zuzulassen, die sich hin-reichend auf diskriminierende Konsequenzen untersuchen lassen. Abstract: Machine Learning can lead to discrimination based on new features that are intransparent. In order to show this, I will argue that discrimination cannot only occur based on features in exhaustive lists but on any feature and that algorithms can be discriminating in spite of a lack of mental attributes. I will show some problems that come with new forms of discrimination that are not easily detectable because of the intransparency of the algorithms. Three forms of intransparency that have different effects on the possibility to detect discriminations have to be distinguished: (i) features of differential treatment can be unknown (ii) features of differential treatment can be unimaginable (they are too complex or "chaotic" to be grasped by humans) (iii) explanations for the use of certain features for differential treatment could lack Especially the combination of intransparency and new features of discrimination is challenging for the philosophical debate about discrimination. Previous solutions were restricted to identify or create imaginable features that are explicitly protected by anti-discrimination law. New forms of discrimination that could have considerable impact in the future are neglected. In order to identify intransparent discrimina-tions unknown features have to be identified, unimaginable features have to be made sufficiently imaginable and correlations and training methods have to be explainable. Then it has to be possible to remove discriminating elements. Insofar no suitable solutions can be found, it must be considered whether we want to allow for specific contexts only those machine learning methods which can sufficiently be examined for discriminating consequences.

There is broad interest in predicting the clinical course of mental disorders from early, multimodal clinical and biological information. Current computational models, however, constitute a significant barrier to realizing this goal. The early identification of trauma survivors at risk of post-traumatic stress disorder (PTSD) is plausible given the disorder's salient onset and the abundance of putative biological and clinical risk indicators. This work evaluates the ability of Machine Learning (ML) forecasting approaches to identify and integrate a panel of unique predictive characteristics and determine their accuracy in forecasting non-remitting PTSD from information collected within10 days of a traumatic event. Data on event characteristics, emergency department observations, and early symptoms were collected in 957 trauma survivors, followed for fifteen months. An ML feature selection algorithm identified a set of predictors that rendered all others redundant. Support Vector Machines (SVMs) as well as other ML classification algorithms were used to evaluate the forecasting accuracy of i) ML selected features, ii) all available features without selection, and iii) Acute Stress Disorder (ASD) symptoms alone. SVM also compared the prediction of a) PTSD diagnostic status at 15 months to b) posterior probability of membership in an empirically derived non-remitting PTSD symptom trajectory. Results are expressed as mean Area Under Receiver Operating Characteristics Curve (AUC). The feature selection algorithm identified 16 predictors, present in !95% cross-validation trials. The accuracy of predicting non-remitting PTSD from that set (AUC ¼ .77) did not differ from predicting from all available information (AUC ¼ .78). Predicting from ASD symptoms was not better then chance (AUC ¼ .60). The prediction of PTSD status was less accurate than that of membership in a non-remitting trajectory (AUC ¼ .71). ML methods may fill a critical gap in forecasting PTSD. The ability to identify and integrate unique risk indicators makes this a promising approach for developing algorithms that infer probabilistic risk of chronic posttraumatic stress psychopathology based on complex sources of biological, psychological, and social information.

The latest advancement in technology and growing data there is a need to extract information in a more efficient and quicker manner using queries. This gives rise to the need for a more easy-to-use query interface. So far, the typical query interfaces are GUI based visual query interfaces. Visual query interfaces however, have limitations especially when they are used for accessing large and complex datasets. The ease of expressing ones queries is limited due to language barrier and the knowledge of precise key words. Therefore, we are developing a novel query interface where users can use natural language expressions to help author visual queries and address the knowledge gap. The opinions and reviews posted in the websites are analyzed mostly based on static data. Till date, the methods applied to determine whether the opinions are positive or negative. But, now a days, as products are increasing day by day and a huge amount of data reviews are available online. So, this mechanism not considers the opinions at comprehensive level.

As a result of latest breakthroughs in technology and dehumanizing efforts, recent developments in artificial intelligence technologies have gained momentum. In particular, the research and development processes related to the proficiency of artificial intelligence in deep learning are being financed by the public and private sectors and an attempt is being made to accelerate the development of deep learning technologies in this direction. These developments in machine learning algorithms offer new ways to manage customer relationships for companies. A chatbot is a chat tool that based on a machine learning algorithm which interacts with people and communicate with them in a natural language on a specific topic. (Huang, 2007) Beginning to use Chatbot services, which emerged as a result of information and communication technologies, in the customer relations services by businesses, makes machine learning algorithms a commercial feature. After Facebook Messenger allowed developers to place Chatbots on their platform in 2016, the number of active Chatbots in the application has exceeded the number of three hundred thousand according to the May 2018 data.

Based on the assumption of using Chatbots in customer relationships may caused different reflections on customers; a research were conducted on the attitudes and perceptions of users in different age groups. In this context, semi-structured interviews were conducted with fifteen people selected from purposive sampling method among different age groups and the obtained information was shared under the findings of the paper.

Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors' and physicians' time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN), Logic Based classifiers such as the Random Forest (RF) algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.

The stock market is known for its extreme complexity and volatility, and people are always
looking for an accurate and effective way to guide stock trading. In addition, LSTM avoids
long-term dependence issues due to its unique storage unit structure, and it helps predict
financial time series. Based on LSTM and an attention mechanism, a wavelet transform is
used to denoise historical stock data, extract and train its features, and establish the prediction
model of a stock price. We have taken Apple Inc. dataset from Yahoo Finance API which
contains the closing stock prices of the company. The accuracy which we achieved is 91%
and the Mean Squared Error (MSE) which we got is 9%.

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In our daily life, economic activities, and national security highly depend on stability, safely, and resilient cyberspace. A network brings communications and transports, power to our homes, run our economy, and provide government with various services.
However it is through the same cyber networks which intrude and attack our privacy, economy, social life in a way which is harmful. Some scholars have interestingly argued that, “in the Internet nobody knows you are a dog”.  This raises some legal issues and concerns.
This paper presents important issues on the Security, Prevention, and Detection of Cyber Crime. The paper consists of four parts in Romanic Numbers.
In Part I, the attempt here is simply to familiarize the reader with a careful understanding of the Cyber Crimes. The author will trace back the meaning, history, and types of Cyber Crimes
In Part II, one has to know the concept of Cyber Security; this will include meaning, background, types if any and important of Cyber Security.
Part III will explain on Cyber Prevention and Detection the same will include meaning and history, Cyber Prevention and Detection Methodologies and Legal Instruments.
And Part IV, Personal Assessment basically in Tanzanian Laws.
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Random forests perform boostrap-aggregation by sampling the training samples with replacement. This enables the evaluation of out-of-bag error which serves as a internal cross-validation mechanism. Our motivation lies in the using of the unsampled training samples to improve the ensemble of decision trees. In this paper we study the effect of using the out-of-bag samples to improve the generalization error first of the decision trees and second the random forest by post-pruning. A preliminary empirical study on four UCI repository datasets show consistent decrease in the size of the forests without considerable loss in accuracy.

Diabetes is a chronic disease with the potential to cause a worldwide health care crisis. According to International Diabetes Federation 382 million people are living with diabetes across the whole world. By 2035, this will be doubled as 592 million. Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood glucose. Various traditional methods, based on physical and chemical tests, are available for diagnosing diabetes. However, early prediction of diabetes is quite challenging task for medical practitioners due to complex interdependence on various factors as diabetes affects human organs such as kidney, eye, heart, nerves, foot etc. Data science methods have the potential to benefit other scientific fields by shedding new light on common questions. One such task is to help make predictions on medical data. Machine learning is an emerging scientific field in data science dealing with the ways in which machines learn from experience. The aim of this project is to develop a system which can perform early prediction of diabetes for a patient with a higher accuracy by combining the results of different machine learning techniques. This project aims to predict diabetes via three different supervised machine learning methods including: SVM, Logistic regression, ANN. This project also aims to propose an effective technique for earlier detection of the diabetes disease.

Over the next years, it is expected that machine learning will be widely implemented within fields in the construction context, such as construction planning. As construction projects tend to be influenced by interrelated issues resulting in cost and/or time overruns and lower performance, it has been continuously attempted to develop predictive planning methods and tools, in order to mitigate such issues. This study aims at investigating possible applications of machine learning for construction planning, noting their impact on project performance, and finally commenting critically on the issues of responsibility in action-taking, accountability in decisionmaking, and the still crucial need for human reasoning. Methodologically, a literature review on machine learning applications in construction project planning is carried out, and then two particular implementation cases are selected for a more in-depth analysis. The first case draws on a productivity survey of construction projects in Sweden, where the relative data is analysed to find the most influential factors behind project performance; then, statistical correlation is used to find the features that are strongly correlated with four performance indicators (cost variance, time variance, and client- and contractor satisfaction), and a supervised machine learning analysis is done to develop a model for predicting project cost, time and satisfaction. The second case elaborates on the appraisal of constructability of civil engineering projects through technical project risk analysis; the model utilizes both unsupervised machine learning for the understanding and pre-processing of data, and supervised machine learning for the development of the predictive system. Following the above analysis, it is argued that there is a need for human reasoning in construction planning, even more so after the introduction of machine learning. It is not enough to include human aspects in the machine learning modelling; it is also crucial to strengthen qualified reasoning in the decision-making for construction project planning and being responsible in action-taking and accountable in decision-making.

The Predictive Airliner is an airline that utilizes the latest technology to deliver an exceptional personalized experience to each and every passenger it flies. Today, technology such as AI, Machine Learning, Augmented Reality, IoT, Real-time stream processing, social media, streaming analytics and wearables are altering the Customer Experience (CX) landscape and airlines need to jump aboard this fast moving technology or run the risk of being left out in the cold. The Predictive Airliner reveals how these and other technologies can help shape the customer journey. The book details how the five types of analytics—descriptive, diagnostic, predictive, prescriptive, and edge analytics—affect not only the customer journey, but also just about every operational function within an airline. An IoT-connected airline can make its operations smart. Data collected at multiple company and customer touch points can be utilized to increase customer satisfaction, as well as make the airline more profitable. The book lays out a blueprint for airlines to use to build a better overall operation. By utilizing AI, machine learning, and deep learning airlines can monitor the health of their airplanes, ensure employee satisfaction, and deliver an award-winning customer experience every time. Analytical processes like decision trees, k-means clustering, logistic regression and neural networks are explained in detail, with specific use cases detailing how they are used profitably in the aviation industry. Edge analytics, sentiment analysis, clickstream analysis, and location analysis are seen through a customer intelligence lens to ensure passengers are treated in a personalized way that will not only increase loyalty but turn passengers into apostles for the airlines they chose to fly on. Connected devices can help with inventory optimization, supply chain management, labor management, waste management, as well as keep the airline’s data centers green and its energy use smart. Social media is no longer a vanity platform, but rather it is a place to both connect with current customers, as well as court new ones. It is also a powerful branding channel that can be utilized to both understand an airline’s position in the market, as well as a place to benchmark its position against competitors. The Predictive Airliner reveals how airlines can utilize this channel in a multitude of ways to connect with customers, as well as help in moments of crisis. Today, technology moves at break-neck speed and it can offer the potential of anticipatory capabilities, but it also comes with a confusing variety of technological terms--Big Data, Cognitive Computing, CX, Data Lakes, Hadoop, Kafka, Personalization, Spark, etc., etc. The Predictive Airliner will help airline executives make sense of it all, so that he or she can cut through the confusing clutter of technological jargon and understand why a Spark-based real-time stream processing data stream might be preferable to a TIBCO Streambase one, or none at all. The final chapter explains how an airline can utilize the concept of the customer journey as a roadmap to increase customer satisfaction. This book will help airline executives break through the technological clutter so that they can deliver an unrivaled customer experience to each and every passenger who steps aboard their planes.

Today, with the development of technology, there are important changes in business life. One of the most important and new changes in business life is the increase in the use of artificial intelligence in human resources. In this study, it is aimed to evaluate the use of artificial intelligence applications in human resources management. Within the scope of the study, personnel recruitment and placement, training and development and performance management were examined. The use of artificial
intelligence applications saves time in these areas and helps human resources teams to work more creatively and strategically for the success of the company. With the use of artificial intelligence, the functioning of human resources has become fast, objective and explainable. With the increasing use of artificial intelligence, we see that human resources are positioned as a new business partner. It is hoped that the study will guide human resource management and academia.

Bu çalışmada 31 makine öğrenmesi algoritması, 22 makine öğrenmesi metodu, 33 Python kütüphanesi, 14 özgün veri seti ile sosyal bilimlerin özellikle ekonomi, finans, işletme alanlarına dair örnek uygulamalar yer almaktadır.

Emlak piyasası, ikinci el araç piyasası, üretim-satış-müşteri süreçlerinin optimizasyonu, döviz ve hisse senedi alım-satımı için algoritmik işlemler ve al-sat sinyalleri oluşturma, makro ekonomi, seçim tahmini ve koalisyon analizi, market sepeti analizi…

Bunun yanı sıra, Python ile arayüz oluşturma, özgün veri seti oluşturmak için web sayfalarından veri “tırtıklama”, veri setlerini Türkçeye çevirme, veri görselleştirme…  gibi uygulamalar da ilaveten ele alındı.

Çalışmayı özgün kılan bir sosyal bilimci tarafından, sosyal bilim araştırmacıları ve  veriye dayalı strateji üretmek isteyen özel sektör temsilcileri için hazırlandı.

Bilimsel bir metodolojiye sahip olmasına rağmen makine öğrenmesi adeta günlük konuşma diline çevrildi. Hikâyeleştirme teknikleri kullanılarak uygulamalar anlatıldı. Eğlenceli bir dil kullanıldı.
Makine öğrenmesinin formüller ve teorik arka planından ziyade sonuçlarına ve uygulayıcı için günlük hayattaki karşılıklarına odaklanıldı. Uzun sıkıcı anlatımlar yerine şema ve görseller kullanıldı.

In this paper; we introduce a system of automatic recognition of characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal and of the Zernike moment. These characteristics are linked to pixels' densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the multi-layer perceptron (MLP) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures.

In this paper; we introduce a system of automatic recognition of characters based on the Random Forest Method in non-constrictive pictures that are stemmed from the terminals Mobile phone. After doing some pretreatments on the picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives of the zoning types, of diagonal, horizontal and of the Zernike moment. These characteristics are linked to pixels' densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the multi-layer perceptron (MLP) and the Random Forest method. After some checking tests, the system of learning and recognition which is based on the Random Forest has shown a good performance on a basis of 100 models of pictures.

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.

Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications.

Anormal Psikoloji kitabı, ruhsal bozukluklar alanında çalışan tüm profesyoneller, psikiyatristler, klinik psikologlar, psikologlar, psikolojik danışmanlar, hekimler, sosyal hizmet uzmanları, psikiyatri hemşireleri ve bu alanlarda öğrenci yetiştiren akademisyenler ile eğitim alan öğrenciler için temel bir başvuru kitabıdır.
2013 yılının Mayısı ayında yayımlanan DSM-5 ölçütlerine göre ruhsal bozukluklarla ilgili sunulan bilgilerin güncellemesinin de yapıldığı bu kitap, tüm dünyada pek çok üniversitenin psikiyatri ve psikoloji bölümlerinde ders kitabı olarak okutulmaktadır.
Kitabın her bölümünde, ilk olarak ruhsal bozukluklarla ilgili vakalar sunulmakta, daha sonra bozuklukların klinik tanıları yapılmakta ve DSM-IV-TR’den farklı olan DSM-5’te yapılan değişikliklere yer verilmektedir. Ayrıca, DSM-5’teki tanı ölçütleri, bozuklukların etiyolojisi ve epidemiyolojisi, bozukluklar üzerinde kalıtımın ve çevrenin etkileri, biyolojik ve sosyal ve kültürel etkiler, en güncel araştırmalardan elde edilen verilerle harmanlanarak sunulmaktadır. Ruhsal bozuklukların tedavisinde etkili olacak terapi yaklaşımları ve ilaçla yapılan tedaviler hakkında da bilgiler veren kitap, sahip olduğu zengin içeriği ile eşsiz bir kaynaktır.
Anormal Psikoloji kitabının; profesyoneller ve öğrenciler için önemli bir rehber olacağına, yardıma ihtiyaç duyan insanların sorunlarının çözümüne, ruhsal bozuklukların tanı ve tedavisinde önemli katkılar sağlayacağına inanıyoruz.

From many years Assisted Instruction (AI) has used to bring the power of the computer system to stand on the educational process. Now a day instead of assisted instruction artificial intelligence methods was applied for the development of intelligent computer assisted instruction (ICAI) systems. It is an attempt to create computerized tutors. This tutors shape the teaching techniques to fit the learning patterns of individual students. This research paper based on the literature reviews available on this emerging topic. The paper gives detail information regarding Artificial Intelligence and machine learning. So that information professionals can use it better way in academic and their research. In this research paper the advantages and disadvantages of artificial intelligence and machine learning are given.

The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.