Artificial Intelligence (AI)

This paper preludes the fuzzy logic algorithms as artificial intelligence (AI) inference engine. That might be the development and implementation of highly superior AI computing machines. For that purpose of the AI model, many fuzzy concepts have been proposed; fuzzy connections (if-then, union and intersection), fuzzy meta-implication, fuzzy inference, fuzzy meta-language and fuzzy predicate. Accordingly, the fuzzy AI model is efficiently implemented in a fuzzy logic controller as a fuzzy algorithm. This algorithm methodology is based on the linguistic approximation to implement the semantic as control commands.

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.

vering sustainable socio-economic development of cities. They are complex environments that are shaped by their innovation capacity, information and communication technologies (ICTs) development  and  adoption,  living  standards,  residents’  readiness,  and,  last  but  not  least,  the willingness to invest. Higher urbanization rates and “mega-cities” with 10 million inhabitants or more make difficult to create a sustainable and cost-effective environment and a high quality of life for the citizens. To overcome this shortcoming, the latest Artificial Intelligence (AI) techniques are needed to increase ICTs solutions and implicitly, to augment the cities competitiveness. Our paper objective is to analyse the public attitude regarding the influence of AI on smart cities characteristics and to identify if there are significant differences in their perspective by gender and by age group. The statistical data analysis was performed using two-way measure analysis of variance (ANOVA). The differences between groups were analysed using inferential statistics. This paper contributes to the understanding of the importance of AI techniques in improving urban living.

This study examines the personality traits of people who tend to engage in social media as opinion leaders in the context of the travel industry. Specifically, we investigate the extent to which openness, exhibitionism, and competence in interpersonal relationships and flow experience affect the propensity of opinion leadership (OLP) and opinion leadership behavior (OLB). The proposed model was tested with two significant populations in social media, Generation X and Generation Y. The results indicate that openness, exhibitionism, and competence in interpersonal relationships significantly increase OLP. We also find that flow experience (i.e., playfulness and time distortion) strengthens the relationship between OLP and OLB. While the model holds true for both populations, the results show that the role of exhibitionism in OLP is more pronounced for Generation X than Generation Y. ------------------------------------------------------------------------------------------------------------------------------------ Citation: Song, S. Y., Cho, E., and Kim, Y-K. (2017). Personality factors and flow affecting opinion leadership in social media. Personality and Individual Differences, 114, 16-23. doi: https://doi.org/10.1016/j.paid.2017.03.058

Artificial intelligence (AI) is one of the leading trends in modern day education system. The prospect of AI in modern day education system is very much important and significant. This paper deals with the different prospects of artificial intelligence (AI) in terms of education process. In this present scenario, AI is being used for the development and progress of teaching learning process which also makes the education easy accessible and effective. Learners are becoming more interested in the use of AI oriented education process. The researcher has also tried to study the future prospects of AI in education system. How the global education system is gradually becoming dependable on the use of AI is also the area of study. Till now AI aren’t capable of replacing teachers but in the upcoming future education sector will be of no use without AI. The present study addresses some genuine and potential aspects of AI in the field teaching learning. The present study is designed in the form of scheduled interview where samples were randomly selected from various schools of West Bengal. The reports of the interviews are studied and understood in the form of case study.

Cybersecurity arguably is the discipline that could benefit most from the introduction of artificial intelligence (AI). Where conventional security systems might be slow and insufficient, artificial intelligence techniques can improve their overall security performance and provide better protection from an increasing number of sophisticated cyber threats. Beside the great opportunities attributed to AI within cybersecurity, its use has justified risks and concerns. To further increase the maturity of cybersecurity, a holistic view of organizations’ cyber environment is required in which AI is combined with human insight, since neither people nor AI alone has proven overall success in this sphere. Thus, socially responsible use of AI techniques will be essential to further mitigate related risks and concerns.

This document examines prominent English theoretical physicist Stephen Hawking's doomsday AI (Artificial Intelligence) predictions, in which he claims that once AI becomes sophisticated enough to outsmart humans in the future, it may pose existential threats to ordinary humans. Hawking's felt that ordinary people might feel outmoded, putting their entire survival in jeopardy. Hawking's forecast of doomsday AI is compared to another widely accepted basic concept, according to which neither the most intelligent nor the strongest will survive, but only those who can adapt to change will. This phrase on adaptability is frequently misattributed to Charles Darwin. However, when we examine Darwin's work, we discover that one of his letters reveals that Darwin believed adaptability to the environment to be one of the fundamentals of survival. While AI may represent an astringent threat to humanity's survival in the future, Hawking's viewpoint ignores the reality that it is the ability to adapt to change, resilience, and self-awareness that distinguishes humans as a distinct species. This document additionally claims that due to its AI addictive nature, and the potential to render humans overly reliant on it, AI would cause them to lose their extremely unique ability to adapt. According to the manuscript, while AI can engender culturally smart humans, it additionally has the potential to adversely impact the intelligence of a human race that will be plenarily reliant on AI for its basics. It is also important that we not only set a one-sided debate about perceiving AI as a threat to humans but also see the flip side of threats to AI from humans.

This document examines prominent English theoretical physicist Stephen Hawking's doomsday AI (Artificial Intelligence) predictions, in which he claims that once AI becomes sophisticated enough to outsmart humans in the future, it may pose existential threats to ordinary humans. Hawking's felt that ordinary people might feel outmoded, putting their entire survival in jeopardy. Hawking's forecast of doomsday AI is compared to another widely accepted basic concept, according to which neither the most intelligent nor the strongest will survive, but only those who can adapt to change will. This phrase on adaptability is frequently misattributed to Charles Darwin. However, when we examine Darwin's work, we discover that one of his letters reveals that Darwin believed adaptability to the environment to be one of the fundamentals of survival. While AI may represent an astringent threat to humanity's survival in the future, Hawking's viewpoint ignores the reality that it is the ability to adapt to change, resilience, and self-awareness that distinguishes humans as a distinct species. This document additionally claims that due to its AI addictive nature, and the potential to render humans overly reliant on it, AI would cause them to lose their extremely unique ability to adapt. According to the manuscript, while AI can engender culturally smart humans, it additionally has the potential to adversely impact the intelligence of a human race that will be plenarily reliant on AI for its basics. It is also important that we not only set a one-sided debate about perceiving AI as a threat to humans but also see the flip side of threats to AI from humans.

In fields such as journalism, the digitalization process runs in parallel to that of robotisation tasks, which facilitate professional work and imply the loss of routine jobs at the same time, but also the need to train in emerging disciplines that are progressively entering media newsrooms. The automation of documentation and news production is based on Artificial Intelligence (AI) and Big Data (BG). The main objective of this article is to search for theoretical references on AI applied to journalism as well as to learn about the case of BeSoccer through a case study and in-depth interviews with its CEO and marketing manager. The results allow us to discover new journalism production forms closely linked to BD and IA

This study uses a human-centered approach to environmental ethics to examine which perceived factors in advertising predict consumers’ intention to purchase “green”, or sustainably and ethically produced, apparel. We use eight different types of green apparel advertisements to build a decision tree model to determine the most influential factors that lead to future purchases of green apparel. We classify consumers’ perceptions of green advertising as either humanistic, environmental, or product-related responses and propose a conceptual framework to outline the essential elements of an effective green advertising strategy. We use a sample of 829 US consumers from the period January 2015 to December 2017 in our empirical research. Our results show that four factors, namely, perception of the apparel’s quality, its uniqueness, caring, and nature connectedness, predict consumers’ intention to purchase green apparel. Notably, the largest segment of consumers (36%), those who perceive high levels of apparel quality and caring in the advertising, are identified as the high-purchase group. Our findings could improve strategies in green apparel advertising by providing a new analytical approach to model consumers’ behavioral intention to purchase green apparel.

Wellbeing has prime significance in our everyday life. Sound wellbeing is important to accomplish the day by day work appropriately. With huge amounts of new human services innovation new businesses, IoT is quickly altering the social insurance diligence. IoT Based Patient Monitoring System utilizing ESP8266 and Arduino. Wellbeing checking is the serious issue in this day and age. Heartbeat rate and internal heat level readings are recorded over ThingSpeak and Google sheets with the goal that quiet wellbeing can be checked from anyplace on the planet over web. we have structured the IoT Based Patient Health Monitoring System utilizing gadgets. This IoT gadget could peruse the beat rate and measure the encompassing temperature. It constantly screens the beat rate and encompassing temperature and updates them to an IoT stage. We are observing different parameters of the patient utilizing web of things. In the patient checking framework dependent on Internet of things venture, the ongoing parameters of patient's wellbeing are sent to cloud utilizing Internet availability. These parameters are sent to a remote Internet area so client can see these subtleties from anyplace on the planet. There is a significant distinction among customary and IOT based patient observing framework. In IOT based framework, subtleties of the patient wellbeing can be seen by numerous clients. The explanation for this is the information should be observed by visiting a site or URL. Though, in GSM based patient checking, the wellbeing parameters are sent utilizing GSM by means of SMS. Health is the most important part of any human’s life without health it is useless to any treasure of life. Most humans live a busy life in which going to a doctor for weekly or even monthly checkup is an impossible task. Without monitoring your health it is not possible to whether you are a healthy or sick person. This problem leads to the design of a product which monitors your health every day without going to a doctor. In this paper, a system is designed as Wellbeing has prime significance in our everyday life. Sound wellbeing is important to accomplish the day by day work appropriately. With huge amounts of new human services innovation new businesses, IoT is quickly altering the social insurance diligence. IoT Based Patient Monitoring System utilizing ESP8266 and Arduino. Wellbeing checking is the serious issue in this day and age. Heartbeat rate and internal heat level readings are recorded over ThingSpeak and Google sheets with the goal that quiet wellbeing can be checked from anyplace on the planet over web. we have structured the IoT Based Patient Health Monitoring System utilizing gadgets. This IoT gadget could peruse the beat rate and measure the encompassing temperature. It constantly screens the beat rate and encompassing temperature and updates them to an IoT stage. We are observing different parameters of the patient utilizing web of things. In the patient checking framework dependent on Internet of things venture, the ongoing parameters of patient's wellbeing are sent to cloud utilizing Internet availability. These parameters are sent to a remote Internet area so client can see these subtleties from anyplace on the planet. There is a significant distinction among customary and IOT based patient observing framework. In IOT based framework, subtleties of the patient wellbeing can be seen by numerous clients. The explanation for this is the information should be observed by visiting a site or URL. Though, in GSM based patient checking, the wellbeing parameters are sent utilizing GSM by means of SMS. Health is the most important part of any human’s life without health it is useless to any treasure of life. Most humans live a busy life in which going to a doctor for weekly or even monthly checkup is an impossible task. Without monitoring your health it is not possible to whether you are a healthy or sick person. This problem leads to the design of a product which monitors your health every day without going to a doctor. In this paper, a system is designed as a prototype for monitoring alerting based on the health of a person. This system is fully automated little or no human help is needed. Any doctor can monitor this person from anywhere through the internet.

Wellbeing has prime significance in our everyday life. Sound wellbeing is important to accomplish the day by day work appropriately. With huge amounts of new human services innovation new businesses, IoT is quickly altering the social insurance diligence. IoT Based Patient Monitoring System utilizing ESP8266 and Arduino. Wellbeing checking is the serious issue in this day and age. Heartbeat rate and internal heat level readings are recorded over ThingSpeak and Google sheets with the goal that quiet wellbeing can be checked from anyplace on the planet over web. we have structured the IoT Based Patient Health Monitoring System utilizing gadgets. This IoT gadget could peruse the beat rate and measure the encompassing temperature. It constantly screens the beat rate and encompassing temperature and updates them to an IoT stage. We are observing different parameters of the patient utilizing web of things. In the patient checking framework dependent on Internet of things venture, the ongoing parameters of patient's wellbeing are sent to cloud utilizing Internet availability. These parameters are sent to a remote Internet area so client can see these subtleties from anyplace on the planet. There is a significant distinction among customary and IOT based patient observing framework. In IOT based framework, subtleties of the patient wellbeing can be seen by numerous clients. The explanation for this is the information should be observed by visiting a site or URL. Though, in GSM based patient checking, the wellbeing parameters are sent utilizing GSM by means of SMS. Health is the most important part of any human’s life without health it is useless to any treasure of life. Most humans live a busy life in which going to a doctor for weekly or even monthly checkup is an impossible task. Without monitoring your health it is not possible to whether you are a healthy or sick person. This problem leads to the design of a product which monitors your health every day without going to a doctor. In this paper, a system is designed as a prototype for monitoring alerting based on the health of a person. This system is fully automated little or no human help is needed. Any doctor can monitor this person from anywhere through the internet.

Machine learning is one of Artificial Intelligence's most exciting new technologies. In many applications, we use algorithms for learning every day. Every time you search a web search engine like Google or Bing, one reason is that you learn how to grade web pages with a learning algorithm, a learning algorithm that is implemented by Google or Microsoft. Facebook identifies pictures of friends and every time it is used, it is also machine learning. Spam filters in email keep the user from getting tons of spam, which is also an algorithm for learning. In this paper, a short review of the broad uses of machine learning and future prospects have been provided. Similar implementations have been illustrated in the same machine learning field. This paper aims to combine all the different fields of technology and to provide a description of the real world applications. In parallel to these two suggestions, proposals were submitted.

This review of human–robot interaction technology (HRI) suggests how retailers can enhance customer service and improve their operations through the use of service robots. We have reviewed earlier studies and have identified current and emerging robotic technologies as potential retail game changers in business. The review of HRI technologies presents actionable information that can help retailers stay relevant with consumers during their technological development of the service system. Although extensive research has investigated the psychological, neurological, and engineering issues of HRI, few studies have established how robot technology can elevate customer service and transform the retail industry. This study explains the technological innovations that enable autonomous robots to offer unique consumer experiences that have noteworthy potential for assisting elderly and physically challenged consumers. This study covers primary HRI applications that can transform retail, entertainment, travel, and service business today. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------                    Citation: Joe, W.Y. and Song, S.Y. (2019). Applying Human-Robot Interaction Technology in Retail Industries. International Journal of Mechanical Engineering and Robotics Research, 8 (6) Link: http://www.ijmerr.com/index.php?m=content&c=index&a=show&catid=171&id=1217

Since more than 20 years Simulation Technologies have been used to educate, train and asses emergency response professionals from Fire & Rescue services to Civil Defence Organisation. It led to different initiatives all over Europe using various technologies and aiming many types of target audiences. This paper aims to start collecting the lessons learned of those virtual simulations projects in order to be able to extract best practise to be shared and spread to all the emergency response community. By using statistics extracted from polls sent in Europe and testimonials from representative virtual simulation training centres as well as results from an experiment, we present a concrete overview of the use of simulation training in the emergency and civil defence in Europe.

Across four studies, we document a novel and unexpected effect such that COVID-19 data gathered by AI (vs. humans) reduces consumers’ intentions to take preventive measures. This effect is driven by greater perceived dehumanization, such that consumers view AI-generated data as numbers, rather than as human lives. We find that this effect is mitigated when humanness is primed, is moderated by AI type (narrow AI leads to greater dehumanization than general AI), and is attenuated by task type (perceived vaccine effectiveness is similar for AI and humans when the vaccine development process highlights AI-advantageous attributes).

Photovoltaic (PV) systems are one of the most important renewable energy resources (RER). It has limited energy efficiency leading to increasing the number of PV units required for certain input power i.e. to higher initial cost. To overcome this problem, maximum power point tracking (MPPT) controllers are used. This work introduces a comparative study of seven MPPT classical, artificial intelligence (AI), and bio-inspired (BI) techniques: perturb and observe (P&O), modified perturb and observe (M-P&O), incremental conductance (INC), fuzzy logic controller (FLC), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and cuckoo search (CS). Under the same climatic conditions, a comparison between these techniques in view of some criteria's: efficiencies, tracking response, implementation cost, and others, will be performed. Simulation results, obtained using MATLAB/SIMULINK program, show that the MPPT techniques improve the lowest efficiency resulted without control. ANFIS is the highest efficiency, but it requires more sensors. CS and ANN produce the best performance, but CS provided significant advantages over others in view of low implementation cost, and fast computing time. P&O has the highest oscillation, but this drawback is eliminated using M-P&O. FLC has the longest computing time due to software complexity, but INC has the longest tracking time. 1. INTRODUCTION Nowadays, renewable energy resources such as photovoltaic (PV) solar energy is one of the most free and friendly source compared to other traditional sources. PV is a static and nonlinear device converts the light (photon) energy directly into electricity and it can be represented as a current source model. The major problems of PV systems are the low conversion efficiency and non-linearity due to characteristics (I-V & P-V curves) is strongly depended on atmospheric conditions (irradiance (G), and temperature (T)) [1, 2]. At the top (knee) of I-V curve, there is a point changes with G and T called maximum power point (MPP) at which the PV source should be operates to produce and transfer maximum power to the load regardless of variations in climatic conditions. A device can track this unique point called maximum power point tracking (MPPT), where it located between PV source and load. It comprises two essential components, DC-DC converter along with MPPT controller. Many MPPT techniques can be used to change the system operating point by controlling the duty cycle and generating a pulse to fire a switch of DC-DC converter [3].

Power system harmonics are a menace to electric power system with disastrous consequence. Due to the presence of non linear load, power quality of the system gets affected. To overcome this, shunt active power filter have been used near harmonic producing loads or at the point of common coupling to block current harmonics. The shunt active power filter is designed to minimize harmonics in source current and reactive power in the non linear power supplies which are creating harmonics. In this paper, Instantaneous power of p-q theory is employed to generate the reference currents and PI controller is used to control the dc link voltage. In addition to this, Artificial Intelligence (AI) technique is used to minimize the harmonics produced by nonlinear load. The main objective of this paper is to analyze and compare THD of the source current with PI controller and by artificial neural network based back propagation algorithm. The proposed system is designed with MATLAB/SIMULINK environment.

The main objective the research is to analyze potential of Artificial Intelligence in order to understand how it can be integrated to improve ergonomic performance and related safety outcomes. Musculoskeletal disorders are the among the most common and expensive health problems related to occupational environments. In work processes, human operators are continuously submitted to physical, environmental, psychological, and psychosocial stress factors that can affect-amongst others-the musculoskeletal system. The effective identification of major factors that can affect the workers' health requires knowledge from several fields, such as anatomy, physics, mathematics, anthropometry, psychology, sociology, etc. On the other hand, Artificial Intelligence has grown significantly in recent years and has penetrated all fields of activity, including the field of industrial ergonomics. The human-"smart" work equipment interaction is becoming more and more a current activity and this interaction must take place in complete safety and health state. The paper aims to investigate how ergonomic performance can be significantly improved by using Artificial Intelligence. The results obtained will be integrated into operational prevention systems that will be developed in the near future.

This study investigating the impact of wind ou sea level rise (SLR) using
Multi/aver Perceptron Neural Network (MLP—NN) at Coastal Area, Sabah. The uìieauî
sea level (MSL) and four meteorology parameters ummnely; wind direction (WD),, it’iuzd
speed (WS), rainfzll and mean cloud cover. These meteorological aramneter and MSL
were monitored regular/v each month over a period from Jan uarv 2007 to Deceniber
2016 at three differeuit locations which is Kudat, Kola Kinabalu and Sandakan. Due to
sinai! amount of data set, hot/i method the ¡111)111 data were divided imito 80 % fbr
tialning and 20% tbr testing data resj)ectivelv.In this study, two scenarios were
introduced; the scenario ¡ (with wind) WD and WS as input parameter while scenario
2 (without wind)rainfiill and miiean cloud cover to predict sea level at each stations.
Then bi using previous mont/i/v sea water level records the model was perfòrmned bi
predicting SLR fon veai 5 years, 1 () vea rs, 3() vea rs, and 50 years ahead in thefuture.  The  performance  of  the models was  evaluated  according  to  three  statistical
indices in terms of the correlation coefficient (R), root mean square error (RMSE) and
scatter  index  (SI).  Investigation  results  indicate  that,  when  compared  to
measurements,  for  50  years  prediction,  all  three models  in  scenario  2  perform well
(with average values of R = 0.6, RMSE = 0.2 cm and SI = 0.4

Enthusiasm for artificial intelligence, machine learning and robotics in day to day life is at an all-time high. Technology giants, big business firms and new start-ups are playing significant role in shaping the financial sector by proving how capable is AI and how human beings and machines can do things together. Widespread of technology at reasonable and affordable cost, quick availability of data and extensive use of ICT tools have brought AI closer to commercial use. Competition in the industry also pave way for AI. In order to exist, survive and gain in the industry, banks and financial institutions enhance their services and ease of access with the adoption of AI applications. In this paper, we have put together a rundown of how Artificial Intelligence (AI) is used in the modern world, specifically in financial sector.

The exponential growth of Automation Machine (which includes Artificial Intelligence (AI) and technologies that are built on AI) has created an atmosphere of Debate Some say that the automatic machines This is going to help drive the economy. And raise the standards of our life, some say that this is the beginning of the intrusion of the machine. We'll all lose our jobs. Some are passive, do not comment on anything. But no matter what kind of opinion we have, sooner or later the era of automation (Automation Era) is coming upon us. When we cannot avoid having to face it we should understand the origins of it more. To be ready for future adaptation let's take a look at the past and the trend of working together. And adaptation of our humans to automation in the last three centuries this little bit. How does AI work? : Artificial intelligence uses machine learning to mimic human intelligence. The computer has to learn how to respond to certain actions, so it uses algorithms and historical data to create something called a propensity model. Propensity models will then start making predictions (like scoring leads or something). AI can do much more than this, but those are common uses and functionality for marketing. And while it might seem like the machines are ready to rise up and take over, humans are still needed to do much of the work. Mainly, we use AI to save us time-adding people to email automation and allowing AI to do much of the work while we work on other tasks. What are the negative impact of AI? : Artificial intelligence (AI) is doing a lot of good and will continue to provide many benefits for our modern world, but along with the good, there will inevitably be negative consequences. The sooner we begin to contemplate what those might be, the better equipped we will be to mitigate and manage the dangers. The first step in being able to prepare for the negative impacts of artificial intelligence is to consider what some of those negative impacts might be. Here are some key ones: 1. AI Bias: Since AI algorithms are built by humans, they can have built-in bias by those who either intentionally or inadvertently introduce them into the algorithm. If AI algorithms are built with a bias or the data in the training sets they are given to learn from is biased, they will produce results that are biased. This reality could lead to unintended consequences like the ones we have seen with discriminatory recruiting algorithms and Microsoft's Twitter Chabot that became racist. As companies build AI algorithms, they need to be developed and trained responsibly. 2. Lose of Certain Job : While many jobs will be created by artificial intelligence and many people predict a net increase in jobs or at least anticipate the same amount will be created to replace the ones that are lost thanks to AI technology, there will be jobs people do today that machines will take over. This will require changes to training and education programmers to prepare our future workforce as well as helping current workers transition to new positions that will utilize their unique human capabilities. 3. A shift in Human Experience: If AI takes over menial tasks and allows humans to significantly reduce the amount of time they need to spend at a job, the extra freedom might seem like a utopia at first glance. However, in order to feel their life has a purpose, humans will need to channel their newfound freedom into new activities that give them the same social and mental benefits that their job used to provide. This might be easier for some people and communities than others. There will likely be economic considerations as well when machines take over responsibilities that humans used to get paid to do. The economic benefits of increased efficiencies are pretty clear on the profit-loss statements of businesses, but the overall benefits to society and the human condition are a bit more opaque.

The purpose of this study is to investigate how law enforcement officers’ attitudes toward occupational attributes of safety, appearance, and comfort affect their wearing behavior related to High-Visibility Safety Apparel (HVSA). The results suggest that attitudes toward safety and appearance positively affect HVSA wearing behavior, while attitudes toward comfort do not. In addition, environmental factors such as providing organizational safety culture, regulation, and training are found to be necessary for changes in an individual’s attitudes and behavior toward HVSA.
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Citation:
Song, S. Y. and Kim, Y-K. (2015). Law Enforcement Officers’ High-Visibility Safety Apparel: The Effect of Their Attitudes on Wearing Behavior. Journal of Textile and Apparel, Technology and Management, 9(2).
doi: http://ojs.cnr.ncsu.edu/index.php/JTATM/article/view/5719

Data mining, known as knowledge discovery is a computer-assisted, analytical process of digging through and analyzing large amount of data and extracting knowledge from the data. Data mining technologies has made many industries like marketing, sales, healthcare organization, financial institutions etc. quite successful. It has a lot of benefits in various fields. It helps in the quick analysis of data and has improved the quality of decision making process and is used to turn information into actionable knowledge. In this paper, data mining process and knowledge discovery process is discussed. Three well known data mining classifier algorithms namely ID3, J48 and Naive Bayes are discussed and their performance has been evaluated using different parameters to find the best algorithm. Further, Naive Bayes classifier algorithm is used for classification of workloads based on different Quality of Service (QoS) parameters.

Artificial Intelligence (AI) and 5G connectivity have been identified as drivers of the so-called Fourth Industrial Revolution (FIR). AI and 5G, through emerging technologies such as blockchain, gene editing, Internet of Things sensors, nanotechnology, or 3D printing accelerate a blurring of boundaries between digital, biological, and physical spheres. In this editorial, we introduce the term boundary object, or boundary technology, that can help process more information (syntactic boundary) for enhanced learning (semantic boundary) and that can create a higher-level intelligence (pragmatic boundary). Boundary objects are also a means of representing, learning about, and transforming knowledge at a given boundary. We propose that crossing syntactic, semantic, and pragmatic boundaries is facilitated by three FIR phenomena (big data, machine learning, and AI). Each of these phenomena possesses a unique capability (processing, learning, and adaptation) to help communities learn about their differences and dependences. We also show how the six articles in this special issue are related to boundary and sphere challenges, and we provide an overview of directions for future research. All in all, marketing scholars should focus on enhancing their abilities in knowledge integration across boundaries to sustain their role as cutting-edge scientists.

Artificial intelligence (AI) is the biggest emerging movement and promise in today's technology world. AI, in contrast to natural (human or animal) intelligence, is intelligence demonstrated by machines. AI is also called machine intelligence, which aims to mimic human intelligence by being able to obtain and apply knowledge and skills. It promises substantial involvements, vast changes, modernisations and integration with and within people's ongoing life. It makes the world more demanding and helps to make prompt and appropriate decisions with real time. This article provides a main analysis of the health industry and healthcare system in Australian healthcare that are relevant to the consequences formed by AI. This article has primarily used the secondary research analysis method to provide a wide-ranging investigation of the positive and negative consequences of health issues relevant to AI, the architects of those consequences and those overstated by the consequences. The secondary resources are subject to journal articles, reports, academic conference proceedings, media articles, corporation-based documents, blogs and other appropriate information. This study found that AI provides useful insights in the Australian healthcare system. It is steadily reducing the cost of the Australian healthcare system and improving patients' overall outcome in Australian healthcare. AI can not only improve the affairs between public and health enterprises but also make life better by increasing efficiency and modernisation. However, beyond the technology maturity, there are still many challenges to overcome before Australian healthcare can fully leverage the potential of AI in healthcare-Ethics being one of the most critical.

Methods of fashion trend forecasting, a global business that focuses on upcoming trends in the field of fashion design and retail, are going to change. But, these change so rapidly these days that it is difficult to predict the upcoming trends in a classic sense. The poor reputation of the fashion industry, which has peaked in the last decade due to its widespread economic and cultural distrust, seems to be changing, but there is still much to be done. The global climate change made more and more people share the view that sustainability is the only way forward for the fashion industry. To reduce the undesirable impact on the environment, and to know (whatever possible precisely) the consumer needs, a 'new-state-of-the-art' is increasing all over the globe. The historic stigma around the procurement of fashion goods and ownership of items with a status symbol value seems to be in progress; the way how people consume has changed. Along with this, the methods of research, forecasting-and foreseeing, expert advice have developed a lot in the past few years, declaring the real presence in the fashion industry of a new culture, aesthetics, and economy-shaping approach. However-due to many short-lived phenomena-, to precisely predict future trends is nearly impossible. Number and quality of methods for foreseeing the future conditions in which the fashion industry will operate are rising day by day; techniques of the skilled predictors are going to further refine and develop in the super-powered digital era. This article seeks to introduce some of the forecasting techniques along with their results. The article also examines the use of the AI in the processes of trend-analysis, and how it is affecting the consumer choice, exploring factors of its future impact on the lifestyle-, consumer-and fashion trends. A discussion of these matters in their correlation is a step towards sustainability in fashion. 1. LIVIN' IN THE AGE OF CHANGE Two words best defining the era we are living in are change and fast. Key to one's success in big part lies in their ability to make fast, well-informed decisions and rapidly to adapt to change. It is valid for Fashion, driven by consumer demand for on-trend and on-time products, forcing the brands and manufacturers to shrink across virtually all categories, and increasing pressure on retail chain players to be trend-conscious, cost-sensitive, but above all: fast-responsive to new demands. The fashion industry, if ever, then nowadays, has become entirely dependent on trend forecasting and data analysis. Governed by the Big Data in retrieving the information on original looks, colors, celebrity wardrobes, and designer collections, consumer trends, and technology innovations, the second largest industry of the world economy is now running a race against time and is looking to algorithm writers for help filtering information. Instead of imagination and creativity, will the data-driven design be the future of fashion? And if so, the next step has already been taken: this will involve AI. The article attempts to introduce some of the factors affecting conditions in which the fashion industry will operate.