Usage Of Intelligence Research Articles

Automatic Identification of Abaca Bunchy Top Disease using Deep Learning Models

Abstract Usage of computer vision and artificial intelligence in the detection and identification of plant diseases has been explored and utilized in agricultural crops and had proven to perform efficiently. However, this disease detection and identification technology has not yet being explored and examined for some economically valuable crops like abaca and banana. This study intended to develop an automatic identification system for Abaca Bunchy Top Disease (ABTD) using different deep learning models. The study utilized a total of 3,840 petioles and petioles with leaves images taken using DSLR and mobile camera. Selected and pre-processed images were then subjected to augmentation techniques, normalization techniques, and morphometric and geometric analyses. Images were then trained using AlexNet, ZFNet, VGG16, and VGG19 architectures and the results were evaluated using Confusion Matrix in terms of accuracy, error rate, and precision. DSLR captured images on leaves and petioles with leaves showed an accuracy greater than 90% in all architectures except VGG16 with only 83% accuracy, while on mobile captured images, leaves showed above 90% accuracy compared to other groups. As to precision, DSLR captured images on petioles showed that out of four architectures, two models showed above 90% precision except for AlexNet and VGG16. However, for mobile captured images, three models showed above 90% precision using petioles image except VGG16. Furthermore, the models can be used for development of software application for detection, monitoring, and evaluation of ABTD.

The effect of Technological, Organizational, Environmental, and Task Technology fit on the Adoption and usage of artificial intelligence (AI) for talent acquisition (TA): Evidence from the Pakistani banking sector.

The effect of Technological, Organizational, Environmental, and Task Technology fit on the Adoption and usage of artificial intelligence (AI) for talent acquisition (TA): Evidence from the Pakistani banking sector.

The Relevance of the Usage of Artificial Intelligence and Machine Learning in Legal Analysis (An Analysis of Legal Provisions in the National Sharia Council Fatwa and Financial Services Authority Regulation Using Plagiarism Checker and ATLAS.ti)

The Relevance of the Usage of Artificial Intelligence and Machine Learning in Legal Analysis (An Analysis of Legal Provisions in the National Sharia Council Fatwa and Financial Services Authority Regulation Using Plagiarism Checker and ATLAS.ti)

Case Study: Comparing Various Safety Features of Cars Across Various Segments of Different Companies

Both Ford and Tata are considered to be one of the biggest and oldest companies in the Automotive Industry. Whenever customer goes into the market in search of a new car, they have a lot of questions and among them one of the questions is that which car in their consideration is the safest and in case of a mishappening would keep them protect them the most. We decided to answer that question across three categories, namely Hatchback, Sedan and SUV by comparing the safety features they offer and the price they charge for them. Today is time of automatic cars, which are driverless cars, which are based on usage of Artificial Intelligence. Also AI based techniques as Artificial neural network are already been used in the optimization of input parameters in conventional machining and unconventional machining, which are used in the manufacturing of cars.

GRA_Net: A Deep Learning Model for Classification of Age and Gender From Facial Images

The problem of gender and age identification has been addressed by many researchers, however, the attention given to it compared to the other related problems of face recognition in particular is far less. The success achieved in this domain has not seen much improvement compared to the other face recognition problems. Any language in the world has a separate set of words and grammatical rules when addressing people of different ages. The decision associated with its usage, relies on our ability to demarcate these individual characteristics like gender and age from the facial appearances at one glance. With the rapid usage of Artificial Intelligence (AI) based systems in different fields, we expect that such decision making capability of these systems match as much as to the human capability. To this end, in this work, we have designed a deep learning based model, called GRA_Net (Gated Residual Attention Network), for the prediction of age and gender from the facial images. This is a modified and improved version of Residual Attention Network where we have included the concept of Gate in the architecture. Gender identification is a binary classification problem whereas prediction of age is a regression problem. We have decomposed this regression problem into a combination of classification and regression problems for achieving better accuracy. Experiments have been done on five publicly available standard datasets namely FG-Net, Wikipedia, AFAD, UTKFAce and AdienceDB. Obtained results have proven its effectiveness for both age and gender classification, thus making it a proper candidate for the same against any other state-of-the-art methods.

Usage of Artificial Intelligence to Prevent and Regulate COVID-19

Introduction: In this current situation where COVID-19 pandemic has affected the whole world, it is high time to take a step to check the expansion of COVID-19 WHO declared it conversely a pandemic and as an emergency The clinicians and scientists in medical industries are observing for the technologies with which they can perform screening on the medical images to detect the COVID-19 virus in a person Objectives: Early identification, screening, acquaintance tracing, prediction, drug/vaccine development and detection with the application of assorted models and algorithms of AI and ML for COVID-19 Methods: Different analyses performed by various researchers using machine learning algorithms and Deep Learning Models using Chest X-rays, Blood Tests have been presented Different techniques and devices used for acquiring the data through various experiments are also presented Results: This paper presented a few techniques followed by various researchers;accuracy, sensitivity, recall achieved by the methodologies have been presented Conclusion: Data collection plays a major role in analyzing the COVID-19 where most of the researches find it very difficult, and early diagnosis also plays a major role in the early detection and control of the widespread of COVID-19 © IJCRR

AI Meets CRNs: A Prospective Review on the Application of Deep Architectures in Spectrum Management

The spectrum low utilization and high demand conundrum created a bottleneck towards fulfilling the requirements of next-generation networks. The cognitive radio (CR) technology was advocated as a de facto technology to alleviate the scarcity and under-utilization of spectrum resources by exploiting temporarily vacant spectrum holes of the licensed spectrum bands. As a result, the CR technology became the first step towards the intelligentization of mobile and wireless networks, and in order to strengthen its intelligent operation, the cognitive engine needs to be enhanced through the exploitation of artificial intelligence (AI) strategies. Since comprehensive literature reviews covering the integration and application of deep architectures in cognitive radio networks (CRNs) are still lacking, this article aims at filling the gap by presenting a detailed review that addresses the integration of deep architectures into the intricacies of spectrum management. This is a prospective review whose primary objective is to provide an in-depth exploration of the recent trends in AI strategies employed in mobile and wireless communication networks. The existing reviews in this area have not considered the relevance of incorporating the mathematical fundamentals of each AI strategy and how to tailor them to specific mobile and wireless networking problems. Therefore, this review addresses that problem by detailing how deep architectures can be integrated into spectrum management problems. Beyond reviewing different ways in which deep architectures can be integrated into spectrum management, model selection strategies and how different deep architectures can be tailored into the CR space to achieve better performance in complex environments are then reported in the context of future research directions.

Application of artificial intelligence in human resource management in the agricultural sector

Digitalization of all spheres of life has become one of the main directions of development in many countries of the world. Artificial intelligence technology has a wide range of applications in various fields. The study aims to identify artificial intelligence usage in human resource management by analysing functions. Qualitative methods are used in the study. As the result, artificial intelligence tools were analysed by matching them on human resource management functions: recruitment, onboarding, assessment, training and development, talent management, salary system, culture and engagement management, management and leadership, misconduct and compliance. Artificial intelligence technologies provide many opportunities for modern companies. Study in detail companies using AI in HR functions and possible changes in the HR manager profession and role of HR in business are future research directions.

Artificial Intelligence and Traditional Machine Learning to Deep Neural Networks: A Study for Social Implications

There are intriguing opportunities on the road to broad usage of artificial intelligence, as well as challenges that must be solved in order to use machine learning and artificial intelligence technology in industries. On the basis of Google Scholar data, this work systematizes artificial intelligence sections and analyzes the dynamics of changes in the number of scientific papers in machine learning sections. The process of data collection and calculation of dynamic indicators of changes in publication activity, such as the growth rate and acceleration of growth of scientific publications, is presented in detail. Particularly evident in the analysis of publishing activity was the high level of interest in current transformer models, the production of datasets for specific industries, and a significant growth in interest in explainable machine learning approaches. As indicated by the negative correlation between the number of publications published and the number of articles published in a given year, relatively minor study topics are gaining an increasing amount of attention. Results demonstrate that, despite the method's limitations, it is possible to (1) identify rapidly expanding fields of research, irrespective of the number of papers published, and (2) anticipate publication activity in the short term with sufficient accuracy for practical purposes. Results for more than 400 search queries relating to designated study topics and the application of machine learning models to industries are presented in this article in a single document. The suggested method assesses the growth and fall of scientific areas that are related with specific key phrases in terms of their dynamics of growth and decline. It does not necessitate access to huge bibliometric archives and enables for the generation of quantitative estimates of dynamic indicators in a reasonably short time frame.

From Amazon.com to Amazon.love: How Alexa is redefining companionship and interdependence for people with special needs

AbstractThe trend of incorporating assistive conversational agents into people's lives has followed the unprecedented expansion in the usage of artificial intelligence (AI). Amazon, in particular, has been a key trendsetter in this area through its Alexa‐powered devices. Alexa is an intelligent personal assistant (IPA) that performs tasks, such as playing music, providing news and information, and controlling smart home appliances. While this IPA is widely utilized, it is especially gaining attention and growing usage by people with special needs. Even though the importance of the utilization of AI by people with special needs has been widely acknowledged in the extant literature, a sizeable gap exists in the marketing literature in relation to the assessment of the managerial and societal implications of IPAs when used by people with special needs. Accordingly, this study aims to examine (a) the stages of relationship development between Alexa and consumers with special needs, and (b) the potential opportunity of this relationship for Amazon in relation to their corporate image. The findings indicate that a relationship between Alexa and consumers with special needs is established as it helps them regain their independence and freedom. This relationship provides an opportunity for Amazon in enhancing its overall image for providing solutions to facilitate the lives of people with special needs.

Automated smart hydroponics system using internet of things

This paper presents a design and implementation of an automated smart hydroponics system using internet of things. The challenges to be solved with this system are the increasing food demand in the world, the need of market of new sustainable method of farming using the Internet of Things. The design was implemented using NodeMcu, Node Red, MQTT and sensors that were chosen during component selection based on required parameters and sending it to the cloud to monitor and be processed. Investigation on previous works done and a review of Internet of Things and Hydroponic systems were done. First the prototype was constructed, programmed and tested, as well as sensors data between two different environments were taken and monitored on cloud-based web page with mobile application. Moreover, a bot has been introduced to control the supply chain and for notification purposes. The system improved its performance and allows it to successfully achieve the aim of the entire system implemented. There are some limitations which can be improved as future work such as including data science with the usage of the artificial intelligence to further improve the crops and get better outcome. Lastly to design end user platform to ease user interaction by using attractive design with no technical configuration involved.

Research Trends on the Usage of Machine Learning and Artificial Intelligence in Advertising

Advertising is a way in which a company introduces possible customers to a company’s product/service, the main objective is possibly to convince them to buy their product or use their service. The significance of Advertising is critical for the company, as this alone can make people aware of the company’s product and in doing so can generate a good possibility of it being sold to the customers. It is inevitable for companies to face changes and one such change is the evolution in the way of doing Advertisement. Advertisement is now done with the help of not so newfound helping hand that is Artificial Intelligence and Machine Learning. The answer to the question as to why the change in the process of Advertising is important lies in the before-after statistical observations of companies using this technology. The results themselves are reasonable motivating factors for companies who are yet to acknowledge the change. The serious challenge to this new version of Advertising is to make sure to not allow the usage of it to such a great extent where ordinary person is concerned about his/her privacy. Implementing Advertisements this way, we are quite sure that making laws, enforcing the laws or even having its own governing body can ensure righteous use of deploying this technology. The future of Advertising is going to be even better than before as Artificial Intelligence and Machine Learning will bring more control of Advertising to companies. Summing up, we feel confident that Advertising with Artificial Intelligence and Machine Learning are here for a noticeable and a significant change.

EMOTIONAL INTELLIGENCE AS PART OF THE STRATEGIC DEVELOPMENT AND MANAGEMENT STYLE IN THE PRE-HIGH EDUCATIONAL SYSTEM IN ALBANIA

In today’s modern world, emotional intelligence (EI) has a strong connection in the work environment and it is considered to be one of the key factors in the well-functioning of institutions, especially of educational ones.
 The main aim of this study is to identify, whether the directors of the schools of the pre-high educational system in Albania are aware of the concept of emotional intelligence, are trained and if they implement it in their leading style, in order to achieve an overall success in schools. The study also aims to identify the differences between new and senior principals in using EI.
 The research methodology is quantitative, 183 questionnaires were filled in by teachers in 11 different schools of the pre high educational institutions in Tirana.
 The instrument used consists of 40 standard statements that cover all five characteristics of the emotional intelligence: self-awareness, self-control, motivation, empathy and social skills, by analyzing the level of agreement in a 1-5 Likert scale.
 The study has shown that principals and teachers of the pre high educational system in Albania have no knowledge on emotional intelligence and as a result they do not implement it. The principals do possess some elements of emotional intelligence as a part of their soft skills, but they are not aware of them and do not use them in increasing the teachers’ work satisfaction.
 Also, there is no difference in the usage of emotional intelligence between young and senior principals, apart from the fact that the seniors have better developed soft skills. This would help them to come closer to the characteristics of emotional intelligence.
 The results of the study imply some recommendations for policy makers, school’s principals and mentor teachers in increasing the use of emotional intelligence in the pre high educational system in Albania. The results confirm the necessity to train the principals about the latest news in the field of education, ethics, communication and leadership with a focus on raising leadership within the focus of Emotional Intelligence. The ministry of Education, Sport and Youth should design policies for the professional development of leaders and teachers in accordance with new management strategies such as emotional intelligence.

A Pragmatic Analytics of Covid Data Patterns for Predictive Mining of Patients

The year 2020 has emerged as a menace and threat for the human being whereby the social as well as professional livings getting affected. In the global perspectives, the human lives are affecting and huge demise occurring. In this research work, the effectual implementation towards the usage of Artificial Intelligence is done with the machine learning so that the overall outcomes and predictive mining can be done with higher degree of performance. The work is having the integration pattern of COVID datasets of patients with benchmark characteristics and thereby to have the predictions for the upcoming tests and by this way overall prediction can be done.

Exploitation of artificial intelligence for predicting the change in air quality and rain fall accumulation during COVID-19

ABSTRACT This article describes the effect of lockdown measures in major cities on air quality along with rainfall accumulation. The impacts COVID-19 on the environment during lockdown conditions have been compared without lockdown conditions with respect to variable time duration and meteorological conditions. Particulate matter concentration of Chennai, Bangalore, Delhi, and Melbourne were assessed during the lockdown period. The mitigation measures taken by governments resulted in improved air quality particularly PM2.5 due to decreased road traffic. In addition, the rainfall and ground water accumulation were investigated during the lockdown period. The finding of this paper signifies the impacts of measures taken by government and provides detail view on death rate with respect to air quality reduction. The rainfall data were accessed in the four parts of India such as the South peninsula, North-West, East and North-East, and Central India. Besides, the anomaly between the observation periods was represented along with the temperature distribution. Compelling all it is evident that the COVID-19 reduced the concentration of the pollutants emitted from various sources. This eventually leads to excess rainfall in most regions around India compared to 2019. On the other end, COVID-19 does not create any significant impact of ozone layer improvement.

Artificial Intelligence in economic decision making: how to assure a trust?

Motivation: The decisions made by modern ‘black box’ artificial intelligence models are not understandable and therefore people do not trust them. This limits down the potential power of usage of Artificial Intelligence. Aim: The idea of this text is to show the different initiatives in different countries how AI, especially black box AI, can be made transparent and trustworthy and what kind of regulations will be implemented or discussed to be implemented. We also show up how a commonly used development process within Machine Learning can be enriched to fulfil the requirements e.g. of the Ethics guidelines for trustworthy AI of the High-Level Expert Group of the European Union. We support our discussion with a proposition of empirical tools providing interpretability. Results: The full potential of AI or products using AI can only be raised if the decision of AI models are transparent and trustworthy. Regulations which are followed over the whole life cycle of AI models, algorithms or the products they using these are therefore necessary as well as understandability or explainability of the decisions these models and algorithms made. Initiatives on every level of stakeholders started, e.g. international level on the European Union, country level, USA, China etc. as well on a company level. The post-hoc local interpretability methods could and should be implemented by economic decision makers to provide compliance with the regulations.

35 Years of (AI) in Geotechnical Engineering: State of the Art

It was 35 years ago since the first usage of Artificial Intelligence (AI) technique in geotechnical engineering, during those years many (AI) techniques were developed based in mathematical, statistical and logical concepts, but the breakthrough occurs by mimicking the natural searching and optimization algorithms. This huge development in (AI) techniques reflected on the geotechnical engineering problems. In this research, 626 paper and thesis published in the period from 1984 to 2019 concerned in applying (AI) techniques in geotechnical engineering were collected, filtered, arranged and classified with respect to subject, (AI) technique, publisher and publishing date and stored in a database. The extracted information from the database were tabulated, presented graphically and commented. The main conclusions is that the number of researches in this field increases almost exponentially, the most used (AI) technique is the Artificial Neural Networks and its enhancements where it is presents about half the researches and finally correlating soil and rock properties is the most addressed subject with about 30% of the researches.

Adoption of AI-based chatbots for hospitality and tourism

PurposeThis study aims to investigate the customers’ behavioral intention and actual usage (AUE) of artificial intelligence (AI)-powered chatbots for hospitality and tourism in India by extending the technology adoption model (TAM) with context-specific variables.Design/methodology/approachTo understand the customers’ behavioral intention and AUE of AI-powered chatbots for tourism, the mixed-method design was used whereby qualitative and quantitative techniques were combined. A total of 36 senior managers and executives from the travel agencies were interviewed and the analysis of interview data was done using NVivo 8.0 software. A total of 1,480 customers were surveyed and the partial least squares structural equation modeling technique was used for data analysis.FindingsAs per the results, the predictors of chatbot adoption intention (AIN) are perceived ease of use, perceived usefulness, perceived trust (PTR), perceived intelligence (PNT) and anthropomorphism (ANM). Technological anxiety (TXN) does not influence the chatbot AIN. Stickiness to traditional human travel agents negatively moderates the relation of AIN and AUE of chatbots in tourism and provides deeper insights into manager’s commitment to providing travel planning services using AI-based chatbots.Practical implicationsThis research presents unique practical insights to the practitioners, managers and executives in the tourism industry, system designers and developers of AI-based chatbot technologies to understand the antecedents of chatbot adoption by travelers. TXN is a vital concern for the customers; so, designers and developers should ensure that chatbots are easily accessible, have a user-friendly interface, be more human-like and communicate in various native languages with the customers.Originality/valueThis study contributes theoretically by extending the TAM to provide better explanatory power with human–robot interaction context-specific constructs – PTR, PNT, ANM and TXN – to examine the customers’ chatbot AIN. This is the first step in the direction to empirically test and validate a theoretical model for chatbots’ adoption and usage, which is a disruptive technology in the hospitality and tourism sector in an emerging economy such as India.

Scaling‐Up Data‐Driven Pilot Projects

Conducting pilot projects are a common approach among organizations to test and evaluate new technology. A pilot project is often conducted to remove uncertainties from a large‐scale project and should be limited in time and scope. Nowadays, several organizations are testing and evaluating artificial intelligence techniques and more advanced forms of analytics via pilot projects. Unfortunately, many organizations are experiencing problems in scaling‐up the findings from pilot projects to the rest of the organization. Hence, results from pilot projects become siloed with limited business value. In this article, we present an overview of barriers for conducting and scaling‐up data‐driven pilot projects. Lack of senior management support is a frequently mentioned top barrier in the literature. In response to this, we present our recommendations on what type of activities can be performed, to increase the chances of getting a positive response from senior management regarding scaling‐up the usage of artificial intelligence and advanced analytics within an organization.

Analysis of COVID-19 Tracking Tool in India

COVID-19 tracking tools or contact-tracing apps are getting developed at a rapid pace by different governments in their respective countries. This study explores one such tool called Aarogya Setu , developed by the Government of India. It is a mobile application developed under the Health Ministry, as a part of the E-Governance initiative, to track and sensitize the citizens of India in a joint battle against COVID-19 spread. The study aims to understand various useful features of this tool and to present different concepts of data science applied within the application along with its importance in managing the ongoing pandemic. The App uses Bluetooth and GPS technologies to alert a user when they are nearby a COVID-19 infected person. The application uses various Data Science concepts such as Classification, Association Rule Mining, and Clustering to analyze COVID-19 spread in India. The study also shows potential upgradations in the application, which includes usage of Artificial Intelligence and Computer Vision to detect COVID-19 patients. The study would be useful for mobile technology professionals, data science professionals, medical practitioners, health-related frontline workers, public administrators, and government officials.