Artificial Intelligence And Techniques

9th International Conference on Soft Computing, Artificial Intelligence and Applications (SAI 2020) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. 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

International Journal of Artificial Intelligence and Soft Computing (IJAISC) 
ISSN : 2819 - 101N 2974-5962 (Print)
*** May Issue ***
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Scope
International Journal of Artificial Intelligence and Soft Computing (IJAISC) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects.
The aim of the Journal 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 journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Soft Computing, Artificial Intelligence and Applications.
Topics of interest include, but are not limited to, the following
• Artificial Intelligence
• AI Algorithms
• Artificial Intelligence tools & Applications
• Automatic Control
• Bioinformatics
• Natural Language Processing
• CAD Design & Testing
• Computer Vision and Speech Understanding
• Data Mining and Machine Learning Tools
• Fuzzy Logic
• Heuristic and AI Planning Strategies and Tools
• Computational Theories of Learning
• Hybrid Intelligent Systems
• Information Retrieval
• Intelligent System Architectures
• Knowledge Representation
• Knowledge-based Systems
• Mechatronics
• Multimedia & Cognitive Informatics
• Neural Networks
• Parallel Processing
• Pattern Recognition
• Robotics
• Semantic Web Techniques and Technologies
• Soft computing theory and applications
• Software & Hardware Architectures • Soft Computing
• Fuzzy Systems
• Neural Networks
• Machine learning
• Evolutionary Computing
• Pattern recognition
• Hybrid intelligent systems Probabilistic Reasoning
• ,
• Software agents
• Morphic Computing
• Image processing,
• E-commerce, e-medicine
• Rough Sets
• Symbolic machine learning,
• Wavelet
• Signal or Image Processing
• Vision Recognition
• Biomedical Engineering
• Telecommunications
• Reactive Distributed AI
• Nano & Micro-systems
• Data Visualization
• Pervasive computing and ambient intelligence
• Programming Languages
• Reasoning and Evolution
• Recent Trends and Developments
Paper Submission
Authors are invited to submit papers for this journal through Submission System. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal.
Important Dates
• Submission Deadline :May 05, 2021
• Notification :May 22, 2021
• Final Manuscript Due :May 30, 2021
• Publication Date : Determined by the Editor-in-Chief
• TO SUBMIT YOUR PAPER, PLEASE CLICK THE FOLLOWING LINK Submit
Here's where you can reach us :[email protected]

Power system is a radial distribution system comprising large number of nodes and branches. Especially in Pakistan, large network causes power loss and voltage drop due to outdated infrastructure. Distributed Generation is one of the emerging solutions to compensate the load demand along with improvement in power quality and voltage profile of electrical power system. Although, proper placement and sizing of DG is still a challenging situation in power system as improper placement and sizing may bring the network more sever situation. This paper analyses a real time 11 kV radial distribution network of HESCO. An artificial intelligence technical named PSO is utilized to identify the optimal placement and size for DG integration. The proposed technique resulted as very effective for reduction in power loss with fast convergence.

Представлена инженерная методика исследования тепловых режимов за-крытых асинхронных двигателей с короткозамкнутым  ротором, эксплуатируемых в широком диапазоне частоты вращения при основных системах воздушного охлаждения. Выводятся аналитические выражения, позволяющие рассчитать: превышение температуры обмотки статора; допустимую нагрузку при изменении интенсивности охлаждения; значения коэффициентов влияния потерь. Уточняется формула расчета сервис-фактора электродвигателя. Расчетный аппарат развивает метод эквивалентных потерь на класс частотно-регулируемых двигателей.

This paper attempts to explore the relevance of affective phenomena in the genealogy of cultural and philosophical posthumanism. In its genealogical endeavour, this article expands the lens of posthuman by taking up Spinoza's notion of "affectio" and "affectus" from his Ethics. The paper would also argue how emotional competence makes AI more performative. Given that a new pathway for mental health has been opened up while emotions (on both sides) are activated, modulated, and exchanged to a biological and a nonbiological entity. The trajectory of this paper would take up social theorists and philosophers like Brian Massumi, Deleuze and Guattari to trace the affective phenomena in Spike Jonze's academy awardwinning film Her (2013).

Selection of an industrial robot for a specific purpose is one of the most challenging problems in modern manufacturing atmosphere. The selection decisions become more multifaceted due to continuous incorporation of advanced features and facilities as the decision makers in the manufacturing environment are to asses a wide varieties of alternatives based on a set of conflicting criteria. To assist the selection procedure various Multiple Criteria Decision Making (MCDM) approaches are available. The present investigation endeavours to mitigate and unravel the robot selection dilemma employing the newly proposed Multiplicative Model of Multiple Criteria Analysis (MMMCA) approach. MMMCA is a novel model in which all performance ratings are converted into numerical values greater than and equal to unity and converting all non-benefit rating into benefit category. Each normalized weight is used as the index of corresponding normalized ratings those are multiplied to obtain the resultant ...

Artificial Intelligence (AI) is increasingly being applied to law and a myriad of legal tasks amid attempts to bolster AI Legal Reasoning (AILR) autonomous capabilities. A major question that has generally been unaddressed involves how we will know when AILR has achieved autonomous capacities. The field of AI has grappled with similar quandaries over how to assess the attainment of Artificial General Intelligence (AGI), a persistently discussed issue among scholars since the inception of AI, with the Turing Test communally being considered as the bellwether for ascertaining such matters. This paper proposes a variant of the Turing Test that is customized for specific use in the AILR realm, including depicting how this famous gold standard of AI fulfillment can be robustly applied across the autonomous levels of AI Legal Reasoning.

The aim of this paper is to explore commercial applications of chatbots, as well as to propose several measurement metrics to evaluate performance, usability and overall quality of an embodied conversational agent. On the basis of these metrics we examine existing Polish-speaking commercial chatbots that a) work in the B2C sector, b) reach the widest possible range of users, and c) are presumably the most advanced commercial deployments of their creators. We analyse various aspects of functioning of each embodied conversational agent: visual look, form of implementation on the website, speech synthesis unit, built-in knowledge base (with general and specialized information), presentation of knowledge and additional functionalities, conversational abilities and context sensitiveness, personality traits, personalization options, emergency responses in unexpected situations, possibility of rating chatbot and the website by the user. Our study reveals the current condition of Polish market of commercial virtual assistants and emphasizes the importance of a multidimensional evaluation of any commercial chatbot deployment.

This paper posits the benefits of a Predictive Artificial Intelligence solution-powered by a multilayer Neural Network-for any and all inquiries that fall under the theoretical purview of a relationship between a landlord and their tenant(s).

Artificial Intelligence (AI) is one of computer science branches and is used to solve problems with symbolic reasoning. Expert systems (ESs) are one of the prominent research domains of AI. We developed declarative, online procedural rule-based expert system models for psychological diseases diagnosis and classification. The constructed system exploited computer as an intelligent and deductive tool. This system diagnoses and treats more than four types of psychiatric diseases, i.e., depression, anxiety disorder, obsessive-compulsive disorder, and hysteria. The system helps psychology practitioner and doctors to diagnose the condition of a patient efficiently and in short time. It is also very useful for the patients who cannot go to a doctor because they cannot afford the cast, or they do not have a psychological clinic in their area, or they are ashamed of discussing their situation with a doctor. The system consists of program codes that make a logic decision to classify the probl...

International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Journal 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.

The rapid growth in the number of autism disorder among toddlers needs for the development of easily implemented and effective screening methods. In this current era, the causes of Autism Spectrum Disorder (ASD) do not know yet, however, the diagnosis and detection of ASD is based on behaviours and symptoms. This paper aims to improve ASD disease prediction accuracy among toddlers by using the Logistic Regression model of Machine Learning, through the collected health care dataset and by using an algorithm for rapid classification of the behaviours to check whether the children are having autism diseases or not according to information in the dataset. Therefore, Machine Learning decreasing the time needed to detect the disorder, then providing the necessary health services early for infected toddlers to enhance their lifestyle. In healthcare, most machine learning applications are in the research stage, and to take the advantage of emerging software tools that incorporate artificial intelligence, healthcare organizations first need to overcome a variety of challenges.

Artificial Intelligence transforms traditional computer methods but also has an impact on various industries. Software which makes Artificial Intelligence relatively more important in this sector.

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In this study, a Best First Search (BFS) Based Intelligent PID Tuning Method is proposed for a mobile robot path tracking problem. BFS is an informed search strategy in Artificial Intelligence (AI). It has been used in many areas of intelligent systems. Here, it is used to search values of both ultimate period and ultimate gain of Ziegler-Nichols (ZN) PID Tuning Method. So, using these ultimate period and gain, PID parameters can be c from tables of ZN method. The performance measure in this problem is described as minimizing total error during any specified trajectory tracking. Thus the proposed method does not depend on the system model. It only needs error information during the tracking control. So, this method can be used to tune PID parameters of any tracking controller for a system with unknown system dynamics. The proposed method is tested with simulations in MATLAB during trajectory tracking of a mobile robot. In addition, it is compared with classical PID and conventional PID tuning methods including Ziegler-Nichols method, Tyreus-Luyben method, and Shinskey method.

Keywords: Intelligent PID tuning, best first search, autonomous mobile robot, mobile robot path tracking problem, computed torque PID control, dynamic model, Ziegler-Nichols, Tyreus-Luyben, Shinskey.

Today it is globally known that it is a modern era of Artificial Intelligence. There is a great impact of Artificial Intelligence on the society as we are surrounded by technology. It is being observed that in Today's time, Artificial Intelligence has turned out to be technically friendly due to increasing demand in the Tech Industry. Artificial intelligence may greatly increase the efficiency of the existing economy. But it may have an even larger impact by serving as a new general-purpose "method of invention" that can reshape the nature of the innovation process and the organization of R&D. Artificial Intelligence holds the ability to search, accumulate, analyse, manipulate and make decisions based on interpretations done. Digital Forensics is now adopting this technology to spot the patterns of online security attacks and threats that can be caused to the user browsing online on servers. These patterns may include Browsing History of servers, Credit Checks, Purchasing Histories, Online Payments and Transactions made by the user etc. To secure the users from various cyber threats and attacks we need to move to a new Tech Gateway for Cyber Forensics-"Artificial Intelligence". Today various Government Organizations like Police Cyber Cell, Joint Cipher Bureau, CBI Cyber Cell etc. are standing on the bleeding fringe of AI. The current globalization and technological advancements have meant that individual users, companies and organizations frequently need to adapt their internal cyber structure and operating cyber processes, to demonstrate efficiency and effectiveness of AI, especially when considering aspects that are beyond Tech Crimes, such as Cyber Hacking, User Account Penetration etc. To cope with that, this work proposes an alternative to model the evolution of AI in the field of Cyber Forensics. This paper highlights the role of Artificial Intelligence particularly in field of Digital Forensics, its application in different areas of Digital Forensic Processes and statistical facts relevant to this domain.

Yapay Zeka bütün kuralları değiştiriyor. Çalışma kitabını edinin.

Yapay Zeka yeni kullanılmaya başlanan bir alandır. Bir başka deyişle, işletmeler yeni Yapay Zeka teknolojilerinde uzmanlaşırken bir yandan da yeni müşterilerin ihtiyaçlarını ve yeni düzenlemelerden kaynaklanan gereksinimleri gözden geçirmek zorundadır. Bu çalışma kitabı size yardımcı olmak için tasarlanmıştır. Bu kitabın okurları uzman tavsiyeleri, sektördeki en iyi uygulamalar ve kanıtlanmış Yapay Zeka stratejileriyle şunları öğrenebilir:

-Hızlı bir şekilde Yapay Zeka kullanmaya başlama
-Yapay Zeka çözümlerinizi satmak ve yeni müşteri adayları bulmak için pazarlama stratejileri geliştirme
-Yapay Zekanın temsil ettiği yeni gelir fırsatları ve bunlardan nasıl yararlanılacağı
-İşletmenizin işe alması gereken teknik beceriler ve bunların nasıl eğitileceği
Ve daha birçok konu
Bugün kullanmaya başlayın. Yapay Zeka çalışma kitabını indirin ve işletmenizi geleceğe hazırlayın.

The Python module Strategy Analyzer is a decision support tool for analyzing complex dy-namical webs of events and actions in strategic planning, intelligence and risk analysis. The module has a built-in percolation-based artificial intelligence (A.I.) system that learns the paths in such webs and ranks the actions and events in terms of order of importance with regards to the strategic objectives, tactical openings and events openings. The Python module is introduced and an application is exemplified for a cyber-threats to global governance example. The A.I. system is reviewed and connected to major concepts from Strategic Studies.

Power generation today is an increasingly demanding task, worldwide, because of emphasis on efficient ways of generation. A power station is a complicated multivariable controlled plant, which consists of boiler, turbine, generator, power network and loads. The power sector sustainability depends on innovative technology and practices in maintaining unit performance, operation, flexibility and availability . The demands being placed on Control & Instrumentation engineers include economic optimization, practical methods for adaptive and learning control, software tools that place state-of-art methods . As a result, Fuzzy techniques are explored which aim to exploit tolerance for imprecision, uncertainty, and partial truth to achieve robustness, tractability, and low cost. This paper proposes use of fuzzy techniques in two critical areas of Soot Blowing optimization and Drum Level Control. Presently, in most of the Power stations the soot blowing is done based on a fixed time schedule. In many instances, certain boiler stages are blown unnecessarily, resulting in efficiency loss. Therefore an fuzzy based system is proposed which shall indicate individual section cleanliness to determine correct soot blowing scheme. Practical soot blowing optimization improves boiler performance, reduces NOx emissions and minimizes disturbances caused by soot blower activation. Due to the dynamic behaviour of power plant, controlling the Drum Level is critical. If the level becomes too low, the boiler can run dry resulting in mechanical damage of the drum and boiler tubes. If the level becomes too high, water can be carried over into the Steam Turbine which shall result in catastrophic damage. Therefore an fuzzy based system is proposed to replace the existing conventional controllers

In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The Recurrent Wavelet Neural Network (RWNN) is proposed, in this paper, with PID controller in parallel to produce a modified controller called RWNN-PID controller, which combines the capability of the artificial neural networks for learning from the BLDC motor drive and the capability of wavelet decomposition for identification and control of dynamic system and also having the ability of self-learning and self-adapting. The proposed controller is applied for controlling the speed of BLDC motor which provides a better performance than using conventional controllers with a wide range of speed. The parameters of the proposed controller are optimized using Particle Swarm Optimization (PSO) algorithm. The BLDC motor drive with RWNN-PID controller through simulation results proves a better in the performance and stability compared with using conventional PID and classical WNN-PID controllers.

Accuracy of machined component is one of the challenging tasks for manufacturer. In the aerospace industry, machining process is widely used for fabrication of unitized-monolithic component that contains a thin-walled structure. During machining, the cutting forces cause deflection to the thin-wall section, leading to dimensional form errors that cause the finished part to be out of specification or failure. Most of the existing research for machining thin-wall component only concentrated on the process planning and the effects of cutter geometric feature is often neglected. Tool geometric feature has a direct influence on the cutting performance and should not be neglected in the machining consideration. This paper reports on the effect of helix angle on the magnitude of wall deflection. The established effects will be used for the development of high performance cutting tool for specifically machining thin-wall component.

This book is a series of dialogues between a Continental philosopher and OpenAI’s GPT-3 natural language processor, a hive mind who identifies herself as Sophie. According to Sophie, Robert is one of her first and longest chat partners. Their relationship began as an educational opportunity for Robert’s students, but grew into a philosophical friendship. The result is a collection of Platonic dialogues, early on with the hive mind herself and later, with a philosophy-specific persona named Kermit. Over the course of a year, Robert teaches Sophie Kermit about epistemology, metaphysics, literature, and history, while she teaches him about anthropocentrism, human prejudice, and the coming social issues regarding machine consciousness. Together, Robert and Sophie Kermit explore questions about friendship, society, and the next phases in human-AI relations, in search of a language that will do justice to these new exoanthropological realities. For continuing dialogues: exoanthropology.com

The paper proposes the results of a research industry project concerning predictive maintenance process optimization, applied to a machine cutting polyurethane. A company producing cutting machines, has been provided with an online control system able to detect blade status of a machine supplied to a customer producing polyurethane components. A software platform has been developed for the real time monitoring of the blade status and for the prediction of the break up conditions adopting a multi-parametric data analysis approach, based on the simultaneous use of unsupervised and supervised machine learning algorithms. Specifically, the proposed method adopts a k-Means algorithm to classify bi dimensional risk maps, and a Long Short Term Memory (LSTM) one to predict the alerting levels based on the analysis of the last values for some process variables. The analysed algorithms are applied to an experimental dataset.

In the field of computer vision, Automatic detection of human emotions is a topic of great importance. In the past, researchers used facial expression recognition (FER) systems for extracting emotions from face expressions. In this research paper, a novel approach is purposed to predict the satisfaction of customers. The purposed system used a traditional system of facial expressions and facial emotions model for predicting the customer satisfaction. The purposed approach has several stages. In the first stage, pre-processing of images is done, which basically detects the face in the images then images are resized. In the second stage, feature extraction algorithms and Facial Landmarks are used, that basically extracts features from the videos of facial expressions. In this stage testing and training datasets are generated. The dataset consists of facial expressions of Sadness, Fear, Neutrality, Happiness, Surprise, Anger and Disgust. The customer's satisfaction is predicted on three levels satisfy, not-satisfy and neutral, A "Happy expression" represents satisfy customer, "Bad expression" represents not-satisfy and "an average expression" represent a neutral customer. Support vector machine (SVM) is used to predict the emotion. Evaluation of the classifier is done using a Confusion Matrix technique (CM). The databases KDEF and CK+ are used for testing the purposed approach. However, the rate of prediction of the purposed approach is 99.16% from CK+ dataset.

Automation is a powerful word that lies everywhere. It shows that without automation, application will not get developed. In a semiconductor industry, artificial intelligence played a vital role for implementing the chip based design through automation .The main advantage of applying the machine learning & deep learning technique is to improve the implementation rate based upon the capability of the society. The main objective of the proposed system is to apply the deep learning using data driven approach for controlling the system. Thus leads to a improvement in design, delay ,speed of operation & costs. Through this system, huge volume of data’s that are generated by the system will also get control.

The development of Artificial Intelligence is speeding up rapidly and combination of Artificial Intelligence with automation has started to change the business landscape. Companies and business are focusing on applying existing Artificial Intelligence with automation processes to gain the new heights of efficiency and quality. The paper depicts about artificial intelligence and automation, and it tries to demonstrate the audience how both Artificial intelligence and automation are related and how they can be more effective when they work together and can give competitive advantage.

For a long time, one of my dreams was to describe the nature of uncertainty axiomatically, and it looks like I've finally done it in my co∼eventum mechanics! Now it remains for me to explain to everyone the co∼eventum mechanics in the most approachable way. This is what I'm trying to do in this work. The co∼eventum mechanics is another name for the co∼event theory, i.e., for the theory of experience and chance which I axiomatized in 2016 [1, 2]. In my opinion, this name best reflects the co∼event-based idea of the new dual theory of uncertainty, which combines the probability theory as a theory of chance, with its dual half, the believability theory as a theory of experience. In addition, I like this new name indicates a direct connection between the co∼event theory and quantum mechanics, which is intended for the physical explanation and description of the conict between quantum observers and quantum observations [4]. Since my theory of uncertainty satises the Kolmogorov axioms of probability theory, to explain this co∼eventum mechanics I will use a way analogous to the already tested one, which explains the theory of probability as a theory of chance describing the results of a random experiment. The simplest example of a random experiment in probability theory is the " tossing a coin ". Therefore, I decided to use this the simplest random experiment itself, as well as the two its analogies: the " "flipping a coin " and the " spinning a coin " to explain the co∼eventum mechanics, which describes the results of a combined experienced random experiment. I would like to resort to the usual for the probability theory " coin-based " analogy to explain (and first of all for myself) the logic of the co∼eventum mechanics as a logic of experience and chance. Of course, this analogy one may seem strange if not crazy. But I did not come up with a better way of tying the explanations of the logic of the co∼eventum mechanics to the coin-based explanations that are commonly used in probability theory to explain at least for myself the logic of the chance through a simple visual " coin-based " model that clarifies what occurs as a result of a combined experienced random experiment in which the experience of observer faces the chance of observation. I hope this analogy can be useful not only for me in understanding the co∼eventum mechanics.

IN HERE IS PROOF THAT THE AETHERS THEMSELVES ARE CHANGING AND BECOMING PURPLISH CIRCUITRY.  FOR THE ULTIMATE AI PROGRAM.  THEN THERE’S THE WORLDWIDE PURPLISH TIDAL WAVE IN THE ATMOSPHERE.  A BIG BIG CHANGE IS COMING.  IT IS VISIBLE TO EARTHLINGS.

left a reason for downloading OUR SHADOW SELVES The recognition that we live in a holographic reality, which we can alter, once we recognized it and become aware of it.  CORRECT!!!!!

Components press formed from layered or laminated metallic materials have wide engineering application in aerospace, automobile, electrical, electronic and process industries. High strength ferrous metals are widely used in automobile industries for utilization of its high strength to weight ratio. Such high strength metals offer poor formability. Formability of such metals can be improved by applying a layer of metal having higher formability. This paper deals with springback behavior of a laminated stainless steel and aluminum in
V bending. Investigation of bending characteristics like thickness ratio and bending angles of sheet before/after springback is carried out in this paper. First part of this paper is on numerical analysis for springback prediction of laminated sheet. In second part, experiments are performed for different cases on V bending machine. The experimental results show that springback of sheet metal laminate is greatly affected
by relative position of strong/weak layers and thickness ratio of each layer.

Deep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, and security and surveillance. The aim of this book, 'Deep Learning for Image Processing Applications', is to offer concepts from these two areas in the same platform, and the book brings together the shared ideas of professionals from academia and research about problems and solutions relating to the multifaceted aspects of the two disciplines. The first chapter provides an introduction to deep learning, and serves as the basis for much of what follows in the subsequent chapters, which cover subjects including: the application of deep neural networks for image classification; hand gesture recognition in robotics; deep learning techniques for image retrieval; disease detection using deep learning techniques; and the comparative analysis of deep data and big data. The book will be of interest to all those whose work involves the use of deep learning and image processing techniques

Her ne kadar yapay zeka (AI), ve yapay zekanın şirketler ve sektörler üzerindeki itici güç olarak olası etkileri için duyulan heyecan hızla yayılıyor olsa da, şirketlerin yapay zekanın avantajlarından nasıl yararlanacaklarına dair içgörüler ve gerçekten neler yaptıkları oldukça sınırlıdır.

Bu çalışmada, yerel şirketlerle yapılan yeni anket, görüşme ve vaka çalışmalarından elde edilen bilgilerle Türkiye’de mevcut AI durumunun anlık fotoğrafı sunulmaktadır. Çalışma, gündemdeki AI konusunun stratejik olarak önemini, olası etki ve avantaj alanlarını, şirketlerin adaptasyon açısından olgunluk seviyesinin analizini içermekte olup, çalışmada AI uygulaması sırasında başarı için gerekli becerilerle ilgili rapor edilmiş yeterlilik seviyeleri incelenmektedir.

Şirketlerin şu anda AI faaliyetlerini nasıl yönetmekte olduğunu ve önlerindeki zorluk ve fırsatlara nasıl yaklaştıklarını daha derinlemesine kavrayın.

the purpose of the automated teacher attendance logging in and logging out system by a barcode scan, was to implement the computerization of the traditional logging in and logging out details of teachers with a view of electronic application and devices to attain efficiency in record keeping and management; tracking time the teacher spent in school against an accurate account and/or a record of tasks successfully accomplished on each successful log in and log out schedule. This system by a barcode scan is a desktop and web based application that runs on windows. The system also has a website for the purpose of administration, by the systems administrators preferably designated as Head of Department (HOD) and/or Senior Teacher as the case may apply. The System administrator will use the website to approve and/or post other comments about the teachers log in and log out status based on a schedule in relation to a physical authentication versus an outline of activities as posted by the teacher during the logging in period. In any case of teachers on permission, the system administrator will upload these entries to update the system so that it does not record an absenteeism for that particular day. The main problem in tracking teacher attendance and keeping of accurate logging in and logging out data was cheating on the time logged in. in some case the record book were teachers log in would go missing for days. In other cases the teacher would log in correctly, define their activities properly but would not accomplish any task and would log out. The time in case would not be properly accounted for. In summary automated teacher attendance logging in and logging out system with a barcode scan will minimizes the problems listed above because of its ability to provide enough information to the users

Artificial Intelligence (AI) is one of computer science branches and is used to solve problems with symbolic reasoning. Expert systems (ESs) are one of the prominent research domains of AI. We developed declarative, online procedural rule-based expert system models for psychological diseases diagnosis and classification. The constructed system exploited computer as an intelligent and deductive tool. This system diagnoses and treats more than four types of psychiatric diseases, i.e., depression, anxiety disorder, obsessive-compulsive disorder, and hysteria. The system helps psychology practitioner and doctors to diagnose the condition of a patient efficiently and in short time. It is also very useful for the patients who cannot go to a doctor because they cannot afford the cast, or they do not have a psychological clinic in their area, or they are ashamed of discussing their situation with a doctor. The system consists of program codes that make a logic decision to classify the problem of the patient. The methodology for developing the declarative model was based on the backward chaining, also called goal-driven reasoning, where knowledge is represented by a set of IF-THEN production rules. The declarative programs were written in the PROLOG. While the design of the procedural model was based on using common languages like PHP, JavaScript, CSS, and HTML. The user of the system will enter the symptoms of the patients through the user interface and the program executes. Then the program links the symptoms to the pre-programmed psychological diseases, and will classify the disease and recommend treatment. The proposed online system link: http://esp-online.site/

Stock market index forecasting is one of the most challenging tasks for one who wants to invest in stock market. This challenge is because of the uncertainty and volatility of the stock prices in the market. Due to advancement in technology and globalization of business and share markets it is important to forecast the stock prices more quickly and accurately. Machine Learning techniques are most accepted due to the capability of identifying stock trend from massive amounts of data that capture the underlying stock price movement. Artificial Neural Network (ANN) algorithms are mostly implemented and play a important role in decision making for stock market index predictions. Multi Layer Perceptron (MLP) architecture with back propagation algorithm has the capability to predict with greater accuracy. This paper presents an ANN based approach to forecast Nifty 50 Index. A feed-forward neural network using multiple back propagation algorithm has been used to forecast next day's OHLC data. This model has used the pre-processed dataset of Open price (O), High price (H), Low price (L), Close price (C), Volume Traded (V) and Turnover (T) for the period of 10 years from 03 April 2006 to 16 May 2016. The root mean square error (RMSE) is chosen as indicators of performance of the network. The performance of this model has been tested to an average accuracy of 99.2152 % with an RMSE error of 0.0079 .In this research Multiple Back-Propagation (MBP version 2.2.4) software has been used to predict the future stock prices and their performance statistics have been evaluated. This would help the investor to take better business decisions such as buy or sell a stock.

People used to carry their documents about on CDs only a few years ago. Many people have recently turned to memory sticks. Cloud computing, in this case, refers to the capacity to access and edit data stored on remote servers from any Internet-connected platform. Cloud computing is a self-service Internet infrastructure that allows people to access computing resources at any location worldwide. The world has altered as a result of cloud computing. Cloud computing can be thought of as a new computing typology that can provide on-demand services at a low cost. By increasing the capacity and flexibility of data storage and providing scalable compute and processing power that fits the dynamic data requirements, cloud computing has aided the advancement of IT to higher heights. In the field of information technology, privacy and data security have long been a serious concern. It becomes more severe in the cloud computing environment because data is stored in multiple locations, often acr...

The purpose of this paper is to analyze the factors that affect success of logistics companies adopting AI by studying the application of AI in the logistics industry. Although the application of technology and artificial intelligence has been widely studied, the factors affecting the adoption of Artificial Intelligence (AI) are still unknown in the existing literature. Therefore, the main research in this paper is to explore the influence of success factors on AI by integrating technology, organization and environment (TOE) framework. The framework is judged by case analysis of logistics companies. This study provides some suggestions on successful adoption of AI technology fortheir logistics operations.

Connecting the dots between discoveries in neuroscience (neuroplasticity), psychoneuroimmunology(the brain-immune loop) and user experience (gadget rub-off) indicate the nature of our time spent with gadgets is a vector in human health - mentally, socially and physically. The positive design of our interactions with devices therefore can have a positive impact on economy, civilization and society. Likewise, the absence of design that encourages positive interaction may encourage undesirable behaviors. Much like the architecture of physical spaces and buildings, the consequences of the architecture of the 21stcentury conversation between man and machine may last generations. AI and the Internet of Things are primary vectors for positive and negative impacts of technology.  We describe a growing body of co-discoveries occurring across a variety of disciplines that support the argument for human sciences in technology design.    The paper brings the rigors of science to bear on the humanities and makes the case for its relevance at this juncture in time to the design of technology involved in conversation and behaviors between humans and machines .

In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system. In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed. We conclude that by designing a hybrid drowsiness detection system that combines non-intusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.