Search results for: artifical intelligence

Authors: Ivanka Valova

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This paper is aimed at using a multi-agent approach to monitor and diagnose a biotechnological system in order to validate certain control actions depending on the process development and the operating conditions. A multi-agent system is defined as a network of interacting software modules that collectively solve complex tasks. Remote monitoring and control of biotechnological processes is a necessity when automated and reliable systems operating with no interruption of certain activities are required. The advantage of our approach is in its flexibility, modularity and the possibility of improving by acquiring functionalities through the integration of artificial intelligence.

Keywords: multi-agent approach, artificial intelligence, biotechnological processes, anaerobic biodegradation

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Authors: Adedayo Johnson Ogungbile, Ayodeji Emmanuel Oke, Oluwaseyi Alabi Awodele

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This study examines the role of emotional intelligence (EI) as a predictor of job satisfaction within the Nigerian construction industry. Utilizing a methodology that combines mean comparison and correlation analysis, the research explores how EI influences job satisfaction across diverse demographic and professional categories. The construction industry, known for its dynamic and often challenging work environment, provides a unique context to investigate how EI contributes to employee satisfaction. The findings reveal a significant positive correlation between EI and job satisfaction across the industry. Gender-based analysis shows that male employees typically report higher EI and job satisfaction levels compared to their female counterparts, although the impact of EI on job satisfaction is more substantial among women. The study further explores the relationship between trait EI and specific job satisfaction categories, identifying a general positive association with overall job satisfaction but not with supervisor-related satisfaction. Employees are categorized into four EI classes, consistently showing that higher EI levels correspond to greater job satisfaction. These findings align with existing literature, underscoring EI's pivotal role in enhancing job satisfaction in the construction sector. The study concludes that fostering EI among construction industry professionals can lead to improved job satisfaction and performance. Consequently, organizations are encouraged to integrate EI development into their professional growth programs to cultivate a more satisfied and effective workforce. In essence, this research highlights the importance of EI as a key predictor of job satisfaction in the Nigerian construction industry, providing valuable insights for both industry stakeholders and researchers into the benefits of prioritizing emotional intelligence in this high-stakes environment.

Keywords: emotional intelligence, job satisfaction, construction industry, workforce productivity, demographics

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Authors: Siyu Wang, Anthony Ward

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Emotional intelligence (EI) is a well-established personal characteristic. It has been viewed as a critical factor which can influence an individual's academic achievement, ability to work and potential to succeed. When working in a group, EI is fundamentally connected to the group members' interaction and ability to work as a team. The ability of a group member to intelligently perceive and understand own emotions (Intrapersonal EI), to intelligently perceive and understand other members' emotions (Interpersonal EI), and to intelligently perceive and understand emotions between different groups (Cross-boundary EI) can be considered as Group emotional intelligence (Group EI). In this research, a more representative Group EI measurement approach, which incorporates the information of the composition of a group and an individual’s role in that group, is proposed. To demonstrate the claim of being more representative Group EI measurement approach, this study adopts a multi-method research design, involving a combination of both qualitative and quantitative techniques to establish a metric of Group EI. From the results, it can be concluded that by introducing the weight coefficient of each group member on group work into the measurement of Group EI, Group EI will be more representative and more capable of understanding what happens during teamwork than previous approaches.

Keywords: case study, emotional intelligence, group EI, multi-method research

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Authors: Viloria Marin Hermes, Paredes Santiago Maritza, Viloria Paredes Jonathan

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The aim of this study is to assess the prevalence of Burnout syndrome in a sample of 600 professors at the University of Guayaquil (Ecuador) using the Maslach Burnout Inventory (M.B.I.). In addition, assessment was made of the effects on health from professional burnout using the General Health Questionnaire (G.H.Q.-28), and the influence of Emotional Intelligence on prevention of its symptoms using the Spanish version of the Trait Meta-Mood Scale (T.M.M.S.-24). After confirmation of the underlying factor structure, the three measurement tools showed high levels of internal consistency, and specific cut-off points were proposed for the group of Latin American academics in the M.B.I. Statistical analysis showed the syndrome is present extensively, particularly on medium levels, with notably low scores given for Professional Self-Esteem. The application of Canonical Correspondence Analysis revealed that low levels of self-esteem are related to depression, with a lack of personal resources related to anxiety and insomnia, whereas the ability to perceive and control emotions and feelings improves perceptions of professional effectiveness and performance.

Keywords: burnout, academics, emotional intelligence, general health, canonical correspondence analysis

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Authors: Yosra Mefteh Rekik, Younes Boujelbene

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We introduce in this paper a new conceptual model representing the stock market dynamics. This model is essentially based on cognitive behavior of the intelligence investors. In order to validate our model, we build an artificial stock market simulation based on agent-oriented methodologies. The proposed simulator is composed of market supervisor agent essentially responsible for executing transactions via an order book and various kinds of investor agents depending to their profile. The purpose of this simulation is to understand the influence of psychological character of an investor and its neighborhood on its decision-making and their impact on the market in terms of price fluctuations. Therefore, the difficulty of the prediction is due to several features: the complexity, the non-linearity and the dynamism of the financial market system, as well as the investor psychology. The Artificial Neural Networks learning mechanism take on the role of traders, who from their futures return expectations and place orders based on their expectations. The results of intensive analysis indicate that the existence of agents having heterogeneous beliefs and preferences has provided a better understanding of price dynamics in the financial market.

Keywords: artificial intelligence methods, artificial stock market, behavioral modeling, multi-agent based simulation

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Authors: Kiran Nair, Jincy George, Suhaib Anagreh

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Artificial intelligence (AI) in a marketing context is a form of a deterministic tool designed to optimize and enhance marketing tasks, research tools, and techniques. It is on the verge of transforming marketing roles and revolutionize the entire industry. This paper aims to explore the current dissemination of the application of artificial intelligence (AI) in the marketing mix, reviewing the scope and application of AI in various aspects of sales and distribution management. The paper also aims at identifying the areas of the strong impact of AI in factors of sales and distribution management such as distribution channel, purchase automation, customer service, merchandising automation, and shopping experiences. This is a qualitative research paper that aims to examine the impact of AI on sales and distribution management of 30 multinational brands in six different industries, namely: airline; automobile; banking and insurance; education; information technology; retail and telecom. Primary data is collected by means of interviews and questionnaires from a sample of 100 marketing managers that have been selected using convenient sampling method. The data is then analyzed using descriptive statistics, correlation analysis and multiple regression analysis. The study reveals that AI applications are extensively used in sales and distribution management, with a strong impact on various factors such as identifying new distribution channels, automation in merchandising, customer service, and purchase automation as well as sales processes. International brands have already integrated AI extensively in their day-to-day operations for better efficiency and improved market share while others are investing heavily in new AI applications for gaining competitive advantage.

Keywords: artificial intelligence, sales and distribution, marketing mix, distribution channel, customer service

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Authors: Saif M. M. Obeidat

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This research investigates the impact of artificial intelligence (AI) on interior spaces within a college of Architecture and Design. Employing a qualitative approach, the study conducts in-depth interviews and reviews AI-integrated design projects within the academic setting. The key objectives include assessing AI integration in design processes, examining the influence of AI on user experience, exploring its role in architectural innovation, identifying challenges, and assessing educational implications. The study aims to provide a comprehensive understanding of AI's role in shaping interior spaces within academia. It anticipates improved efficiency in design processes, positive user feedback on functionality and experiences, the emergence of innovative design solutions, and the identification of challenges like ethical considerations and technical limitations. Additionally, the research expects insights into how educational programs may need to adapt to incorporate AI knowledge and skills, ensuring students are well-prepared for the evolving landscape of architecture and design practice. By addressing these objectives, the research contributes valuable insights into the evolving relationship between technology and the field of architecture, particularly within educational contexts.

Keywords: interior design, artificial intelligence, academic settings, technology, education

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Authors: Thomas Rockstuhl, Soon Ang, Kok Yee Ng, Linn Van Dyne

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Intercultural interactions are often challenging and fraught with conflicts. To shed light on how to interact effectively across cultures, academics and practitioners alike have advanced a plethora of intercultural competence models. However, the majority of this work has emphasized distal outcomes, such as job performance and cultural adjustment, rather than proximal outcomes, such as how individuals resolve inevitable intercultural conflicts. As a consequence, the processes by which individuals negotiate challenging intercultural conflicts are not well understood. The current study advances theorizing on intercultural conflict resolution by exploring antecedents of how people resolve intercultural conflicts. To this end, we examine creativity – the generation of novel and useful ideas – in the context of resolving cultural conflicts in intercultural interactions. Based on the dual-identity theory of creativity, we propose that individuals with greater international experience will display greater creativity and that the relationship is accentuated by individual’s cultural intelligence. Two studies test these hypotheses. The first study comprises 84 senior university students, drawn from an international organizational behavior course. The second study replicates findings from the first study in a sample of 89 executives from eleven countries. Participants in both studies provided protocols of their strategies for resolving two intercultural conflicts, as depicted in two multimedia-vignettes of challenging intercultural work-related interactions. Two research assistants, trained in intercultural management but blind to the study hypotheses, coded all strategies for their novelty and usefulness following scoring procedures for creativity tasks. Participants also completed online surveys of demographic background information, including their international experience, and cultural intelligence. Hierarchical linear modeling showed that surprisingly, while international experience is positively associated with usefulness, it is unrelated to novelty. Further, a person’s cultural intelligence strengthens the positive effect of international experience on usefulness and mitigates the effect of international experience on novelty. Theoretically, our findings offer an important theoretical extension to the dual-identity theory of creativity by identifying cultural intelligence as an important individual difference moderator that qualifies the relationship between international experience and creative conflict resolution. In terms of novelty, individuals higher in cultural intelligence seem less susceptible to rigidity effects of international experiences. Perhaps they are more capable of assessing which aspects of culture are relevant and apply relevant experiences when they brainstorm novel ideas. For utility, individuals high in cultural intelligence are better able to leverage on their international experience to assess the viability of their ideas because their richer and more organized cultural knowledge structure allows them to assess possible options more efficiently and accurately. In sum, our findings suggest that cultural intelligence is an important and promising intercultural competence that fosters creative resolutions to intercultural conflicts. We hope that our findings stimulate future research on creativity and conflict resolution in intercultural contexts.

Keywords: cultural Intelligence, intercultural conflict, intercultural creativity, international experience

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Authors: Mehvish Bilal, Navneet Singh, Jasir Mushtaq

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Water pollution puts people's health at risk, and it can also impact the ecology. For practitioners of integrated water resources management (IWRM), water quality modelling may be useful for informing decisions about pollution control (such as discharge permitting) or demand management (such as abstraction permitting). To comprehend the current pollutant load, movement of effective load movement of contaminants generates effective relation between pollutants, mathematical simulation, source, and water quality is regarded as one of the best estimating tools. The current study involves the Qual2k model, which includes manual simulation of the various physiochemical characteristics of water. To this end, various sensors could be installed for the automatic simulation of various physiochemical characteristics of water. An artificial intelligence model has been proposed for the automatic simulation of water quality parameters. Models of water quality have become an effective tool for identifying worldwide water contamination, as well as the ultimate fate and behavior of contaminants in the water environment. Water quality model research is primarily conducted in Europe and other industrialized countries in the first world, where theoretical underpinnings and practical research are prioritized.

Keywords: artificial intelligence, QUAL2K, simulation, physiochemical parameters

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Authors: Mahshid TalebianKiakalayeh

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As AI technologies can be used by both civilians and soldiers, it is vital to consider the consequences emanating from AI military as well as civilian use. Indeed, many of the same technologies can have a dual-use. This paper will explore the military uses of AI and assess its compliance with international legal norms. AI developments not only have changed the capacity of the military to conduct complex operations but have also increased legal concerns. The existence of a potential legal vacuum in legal principles on the military use of AI indicates the necessity of more study on compliance with International Humanitarian Law (IHL), the branch of international law which governs the conduct of hostilities. While capabilities of new means of military AI continue to advance at incredible rates, this body of law is seeking to limit the methods of warfare protecting civilian persons who are not participating in an armed conflict. Implementing AI in the military realm would result in potential issues, including ethical and legal challenges. For instance, when intelligence can perform any warfare task without any human involvement, a range of humanitarian debates will be raised as to whether this technology might distinguish between military and civilian targets or not. This is mainly because AI in fully military systems would not seem to carry legal and ethical judgment, which can interfere with IHL principles. The paper will take, as a case study, Canada’s compliance with IHL in the area of AI and the related legal issues that are likely to arise as this country continues to develop military uses of AI.

Keywords: artificial intelligence, military use, international humanitarian law, the Canadian perspective

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Authors: Kudzanayi Chiteka, Wellington Makondo

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The whole world is on the drive to go green owing to the negative effects of burning fossil fuels. Therefore, there is immediate need to identify and utilise alternative renewable energy sources. Among these energy sources solar energy is one of the most dominant in Zimbabwe. Solar power plants used to generate electricity are entirely dependent on solar radiation. For planning purposes, solar radiation values should be known in advance to make necessary arrangements to minimise the negative effects of the absence of solar radiation due to cloud cover and other naturally occurring phenomena. This research focused on the prediction of Global Horizontal Irradiation values for the sixth day given values for the past five days. Artificial intelligence techniques were used in this research. Three models were developed based on Support Vector Machines, Radial Basis Function, and Feed Forward Back-Propagation Artificial neural network. Results revealed that Support Vector Machines gives the best results compared to the other two with a mean absolute percentage error (MAPE) of 2%, Mean Absolute Error (MAE) of 0.05kWh/m²/day root mean square (RMS) error of 0.15kWh/m²/day and a coefficient of determination of 0.990. The other predictive models had prediction accuracies of MAPEs of 4.5% and 6% respectively for Radial Basis Function and Feed Forward Back-propagation Artificial neural network. These two models also had coefficients of determination of 0.975 and 0.970 respectively. It was found that prediction of GHI values for the future days is possible using artificial intelligence-based predictive models.

Keywords: solar energy, global horizontal irradiation, artificial intelligence, predictive models

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Authors: Joshua Osondu

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This research explores how Artificial Intelligence (AI) can be utilized as a policy response to address teaching and learning (TL) issues in education in Ghana. The dual (AI and human) instructor model is used as a theoretical framework to examine how AI can be employed to improve teaching and learning processes and to equip learners with the necessary skills in the emerging AI society. A qualitative research design was employed to assess the impact of AI on various TL issues, such as teacher workloads, a lack of qualified educators, low academic performance, unequal access to education and educational resources, a lack of participation in learning, and poor access and participation based on gender, place of origin, and disability. The study concludes that AI can be an effective policy response to TL issues in Ghana, as it has the potential to increase students’ participation in learning, increase access to quality education, reduce teacher workloads, and provide more personalized instruction. The findings of this study are significant for filling in the gaps in AI research in Ghana and other developing countries and for motivating the government and educational institutions to implement AI in TL, as this would ensure quality, access, and participation in education and help Ghana industrialize.

Keywords: artificial intelligence, teacher, learner, students, policy response

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Authors: Boris M. Fedorov, Liudmila P. Goncharenko, Sergey A. Sybachin, Natalia A. Mamedova, Ekaterina V. Makarenkova, Saule Rakhimova

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In modern economic conditions, the question of the possibility of correct recognition of a signature on digital documents in order to verify the expression of will or confirm a certain operation is relevant. The additional complexity of processing lies in the dynamic variability of the signature for each individual, as well as in the way information is processed because the signature refers to biometric data. The article discusses the issues of using artificial intelligence models in order to improve the quality of signature confirmation in document recognition. The analysis of several possible options for using the model is carried out. The results of the study are given, in which it is possible to correctly determine the authenticity of the signature on small samples.

Keywords: signature recognition, biometric data, artificial intelligence, neural networks

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Authors: Paridhi Agarwal, Kusum M. George

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A cynic is someone upset about the future prematurely. In today’s highly competitive workplace, cynicism has become a prominent concern. It is a controversial issue that brings about psychological disengagement and antagonism towards the management. In organizational sciences, scientific investigation of this negative work behavior is lacking, and so there is no universal definition so far. But most commonly, Organizational Cynicism (OC) has been characterized as an unfavorable attitude towards the organization, encompassing a belief that the organization has low integrity, negative affect, and depreciative behavioral tendencies. Given its prevalence, this study aims to contribute to the existing body of knowledge on OC. This research examines the predictability of OC from two factors- Perceived Organizational Support (POS) and Emotional Intelligence (EI) among millennials in India as well as identify contradictions in today’s scenario. Standardized Organizational Cynicism Scale comprising of three components, Perceived Organizational Support Questionnaire and Goleman’s Emotional Intelligence Test are used on a convenient sample of 104 corporate sector employees in the age range 22-35 years. Correlation test elucidated the relationships, and regression analysis revealed the level of influence of the above variables on OC. Surprisingly, Emotional-Social Awareness had stronger relationships with all dimensions of OC in males as compared to females. It was also seen that EI and POS, together with predicted OC, but separately, only POS accounted for variability in OC, and this impact was much stronger for males, implying that there are other important factors that make females cynical at work. Thus, the over-emphasis on EI training for the millennial generation has also been challenged in this study. It can be said that there are avertible preconditions to the negative attitude- OC. This research has important managerial implications in areas of recruitment, training, and organizational environment.

Keywords: emotional intelligence, millennials, organizational cynicism, perceived organizational support.

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Authors: Angelis P. Barlampas

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Target: The (SARS-CoV-2) is still a threat. AI software could be useful, categorizing the disease into different severities and indicate the extent of the lesions. Materials and methods: AI is a new revolutionary technique, which uses powered computerized systems, to do what a human being does more rapidly, more easily, as accurate and diagnostically safe as the original medical report and, in certain circumstances, even better, saving time and helping the health system to overcome problems, such as work overload and human fatigue. Results: It will be given an effort to describe to the inexperienced reader (see figures), as simple as possible, how an artificial intelligence system diagnoses computed tomography pictures. First, the computerized machine learns the physiologic motives of lung parenchyma by being feeded with normal structured images of the lung tissue. Having being used to recognizing normal structures, it can then easily indentify the pathologic ones, as their images do not fit to known normal picture motives. It is the same way as when someone spends his free time in reading magazines with quizzes, such as <> and <>. General conclusion: The AI mimics the physiological processes of the human mind, but it does that more efficiently and rapidly and provides results in a few seconds, whereas an experienced radiologist needs many days to do that, or even worse, he is unable to accomplish such a huge task.

Keywords: covid-19, artificial intelligence, automated imaging, CT, chest imaging

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Authors: Urva Maryam

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The information has become an important asset to the current cosmos. Globally, various tactics are being observed to confine the spread of information as it makes people vulnerable to security attacks. Open Source Intelligence (OSINT) is a publicly available source that has disseminated information about users or websites, companies, and various organizations. This paper focuses on the quantitative method of exploring various OSINT tools that reveal public information of personals. This information could further facilitate phishing attacks. Phishing attacks can be launched on email addresses, open ports, and unsecure web-surfing. This study allows to analyze the information retrieved from OSINT tools, i.e. theHarvester, and Maltego that can be used to send phishing attacks to individuals.

Keywords: e-mail spoofing, Maltego, OSINT, phishing, spear phishing, theHarvester

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Authors: Agada N. Ihuoma, Nagata Yasunori

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Artificial intelligence (AI) techniques play a crucial role in predicting the expected energy outcome and its performance, analysis, modeling, and control of renewable energy. Renewable energy is becoming more popular for economic and environmental reasons. In the face of global energy consumption and increased depletion of most fossil fuels, the world is faced with the challenges of meeting the ever-increasing energy demands. Therefore, incorporating artificial intelligence to predict solar radiation outcomes from the intermittent sunlight is crucial to enable a balance between supply and demand of energy on loads, predict the performance and outcome of solar energy, enhance production planning and energy management, and ensure proper sizing of parameters when generating clean energy. However, one of the major problems of forecasting is the algorithms used to control, model, and predict performances of the energy systems, which are complicated and involves large computer power, differential equations, and time series. Also, having unreliable data (poor quality) for solar radiation over a geographical location as well as insufficient long series can be a bottleneck to actualization. To overcome these problems, this study employs the anaconda Navigator (Jupyter Notebook) for machine learning which can combine larger amounts of data with fast, iterative processing and intelligent algorithms allowing the software to learn automatically from patterns or features to predict the performance and outcome of Solar Energy which in turns enables the balance of supply and demand on loads as well as enhance production planning and energy management.

Keywords: artificial Intelligence, backward elimination, linear regression, solar energy

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Authors: Fanny Soulard

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Today work environment is more multicultural than ever: spatial limits have been blown out, encouraging people and ideas mobility all around the globe. Indeed, opportunities to design with culturally diverse team workers, clients, or end-users, have become within everyone's reach. We enjoy traveling to discover other civilizations, but when it comes to business, we often take for granted that our own work methodology will be generic enough to federate each party and cover the project needs. This paper aims to explore why, by skipping cultural awareness, we often create misunderstandings, frustration, and even counterproductive design. Tree lighting projects successively developed by a French lighting studio, a Vietnamese lighting studio, and an Australian Engineering company will be assessed from their concept stage to completion. All these study cases are based in Vietnam, where the construction market is equally led by local and international consultants. Core criteria such as lighting standard reference, service scope, communication tools, internal team organization, delivery package content, key priorities, and client relationship will help to spot and list when and how cultural diversity has impacted the design output and effectiveness. On the second hand, we will demonstrate through the same selected projects how intercultural intelligence tools and mindset can not only respond positively to previous situations and avoid major clashes but also turn cultural differences into a key added value to generate significant benefits for individuals, teams, and companies. By understanding the major importance of including a cultural factor within any design, intercultural intelligence will quickly turn out as a “must have” skill to be developed and acquired by any designer.

Keywords: intercultural intelligence, lighting design, work methodology, multicultural diversity

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Authors: Maciej Buczowski

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This study investigates the impact of non-verbal communication training on enhancing the emotional intelligence (EI) of participants in higher education exchange programs. Recognizing the vital role EI plays in academic and professional success, particularly in multicultural environments, this research aims to explore the interplay between non-verbal cues and EI. Utilizing a longitudinal mixed-methods approach, the study will assess EI development over time among international students and faculty members. Participants will undergo a comprehensive non-verbal communication training program, covering modules on recognizing and interpreting emotional expressions, understanding cultural variations, and using non-verbal cues to manage interpersonal dynamics. EI levels will be measured using established instruments such as the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) and the Emotional Quotient Inventory (EQ-i), supplemented by qualitative data from interviews and focus groups. A control group will be included to validate the intervention's effectiveness. Data collection at multiple time points (pre-training, mid-training, post-training, and follow-up) will enable tracking of EI changes. The study hypothesizes significant improvements in participants' EI, particularly in emotional awareness, empathy, and relationship management, leading to better academic performance and increased satisfaction with the exchange experience. This research aims to provide insights into the relationship between non-verbal communication and EI, potentially influencing the design of exchange programs to include EI development components and enhancing the effectiveness of international education initiatives.

Keywords: emotional intelligence, higher education exchange program, non-verbal communication, intercultural communication, cognitive linguistics

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Authors: Thewodros K. Geberemariam

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The rapid physical expansion of urbanization coupled with aging infrastructure presents a unique decision and management challenges for many big city municipalities. Cities must therefore upgrade and maintain the existing aging urban drainage infrastructure systems to keep up with the demands. Given the overall contribution of assets to municipal revenue and the importance of infrastructure to the success of a livable city, many municipalities are currently looking for a robust and smart urban drainage infrastructure asset management solution that combines management, financial, engineering and technical practices. This robust decision-making shall rely on sound, complete, current and relevant data that enables asset valuation, impairment testing, lifecycle modeling, and forecasting across the multiple asset portfolios. On this paper, predictive computational intelligence (CI) and multi-class machine learning (ML) coupled with online, offline, and historical record data that are collected from an array of multi-parameter sensors are used for the extraction of different operational and non-conforming patterns hidden in structured and unstructured data to determine and produce actionable insight on the current and future states of the network. This paper aims to improve the strategic decision-making process by identifying all possible alternatives; evaluate the risk of each alternative, and choose the alternative most likely to attain the required goal in a cost-effective manner using historical and near real-time urban drainage infrastructure data for urban drainage infrastructures assets that have previously not benefited from computational intelligence and machine learning advancements.

Keywords: computational intelligence, machine learning, urban drainage infrastructure, machine learning, classification, prediction, asset management space

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Authors: Giuseppina Settanni, Antonio Panarese, Raffaele Vaira, Maurizio Galiano

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Nowdays, artificial intelligence is used successfully in academia and industry for its ability to learn from a large amount of data. In particular, in recent years the use of machine learning algorithms in the field of e-commerce has spread worldwide. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a chatbot and a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. The recommendation systems perform the important function of automatically filtering and personalizing information, thus allowing to manage with the IT overload to which the user is exposed on a daily basis. Recently, international research has experimented with the use of machine learning technologies with the aim to increase the potential of traditional recommendation systems. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Artificial intelligence algorithms have been implemented and trained on historical data collected from user browsing. Finally, the testing phase allowed to validate the implemented model, which will be further tested by letting customers use it.

Keywords: machine learning, recommender system, software platform, support vector machine

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Authors: Sayor Ajfar Aaron, Sajjat Hossain Abir, Ashif Newaz, Mushfiqur Rahman

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Investigating the convergence of blockchain technology and artificial intelligence, this paper examines their synergistic effects on data integrity and decision-making within smart contracts. By implementing AI-driven analytics on blockchain-based platforms, the research identifies improvements in automated contract enforcement and decision accuracy. The paper presents a framework that leverages AI to enhance transparency and trust while blockchain ensures immutable record-keeping, culminating in significantly optimized operational efficiencies in various industries.

Keywords: artificial intelligence, blockchain, data integrity, smart contracts

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Authors: Urva Maryam

Abstract:

Information has become an important asset to the current cosmos. Globally, various tactics are being observed to confine the spread of information as it makes people vulnerable to security attacks. Open Source Intelligence (OSINT) is a publicly available source that has disseminated information about users or website, companies, and various organizations. This paper focuses on the quantitative method of exploring various OSINT tools that reveal public information of personals. This information could further facilitate the phishing attacks. Phishing attacks can be launched on email addresses, open ports, and unsecured web-surfing. This study allows to analyze information retrieved from OSINT tools i.e., the Harvester, and Maltego, that can be used to send phishing attacks to individuals.

Keywords: OSINT, phishing, spear phishing, email spoofing, the harvester, maltego

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Authors: Mokrane Selma

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The shift towards decision making (DM) based on artificial intelligence (AI) techniques will change the way in which consumer markets and our societies function. Through AI, predictive analytics is being used by businesses to identify these patterns and major trends with the objective to improve the DM and influence future business outcomes. This paper proposes an Artificial Neural Network (ANN) approach to predict the success of telemarketing calls for selling bank long-term deposits. To validate the proposed model, we uses the bank marketing data of 41188 phone calls. The ANN attains 98.93% of accuracy which outperforms other conventional classifiers and confirms that it is credible and valuable approach for telemarketing campaign managers.

Keywords: bank telemarketing, prediction, decision making, artificial intelligence, artificial neural network

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Authors: S. Tehsin, S. Kausar

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The ultimate aim of machine intelligence is to explore and materialize the human capabilities, one of which is the ability to detect various text objects within one or more images displayed on any canvas including prints, videos or electronic displays. Multimedia data has increased rapidly in past years. Textual information present in multimedia contains important information about the image/video content. However, it needs to technologically testify the commonly used human intelligence of detecting and differentiating the text within an image, for computers. Hence in this paper feature set based on anatomical study of human text detection system is proposed. Subsequent examination bears testimony to the fact that the features extracted proved instrumental to text detection.

Keywords: biologically inspired vision, content based retrieval, document analysis, text extraction

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Authors: Luis Rodrigo Valencia Perez, Francisco Flores Aguero, Gibran Aguilar Rangel

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Artificial Intelligence (AI) is rapidly transforming diverse sectors, and higher education in Latin America is no exception. This article explores the impact of AI on higher education institutions in the region, highlighting the imperative need for well-trained teachers in emerging technologies and a cultural shift towards the adoption and efficient use of these tools. AI offers significant opportunities to improve learning personalization, optimize administrative processes, and promote more inclusive and accessible education. However, the effectiveness of its implementation depends largely on the preparation and willingness of teachers to integrate these technologies into their pedagogical practices. Furthermore, it is essential that Latin American countries develop and implement public policies that encourage the adoption of AI in the education sector, thus ensuring that institutions can compete globally. Policies should focus on the continuous training of educators, investment in technological infrastructure, and the creation of regulatory frameworks that promote innovation and the ethical use of AI. Only through a comprehensive and collaborative approach will it be possible to fully harness the potential of AI to transform higher education in Latin America, thereby boosting the region's development and competitiveness on the global stage.

Keywords: artificial intelligence (AI), higher education, teacher training, public policies, latin america, global competitiveness

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Authors: Genady Grabarnik, Serge Yaskolko

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Generative Artificial Intelligence (AI) has significantly expanded its applicability with the incorporation of Large Language Models (LLMs) and become a technology with promise to automate some areas that were very difficult to automate before. The paper describes the introduction of generative Artificial Intelligence into Introductory Computer and Data Science courses and analysis of effect of such introduction. The generative Artificial Intelligence is incorporated in the educational process two-fold: For the instructors, we create templates of prompts for generation of tasks, and grading of the students work, including feedback on the submitted assignments. For the students, we introduce them to basic prompt engineering, which in turn will be used for generation of test cases based on description of the problems, generating code snippets for the single block complexity programming, and partitioning into such blocks of an average size complexity programming. The above-mentioned classes are run using Large Language Models, and feedback from instructors and students and courses’ outcomes are collected. The analysis shows statistically significant positive effect and preference of both stakeholders.

Keywords: introductory computer and data science education, generative AI, large language models, application of LLMS to computer and data science education

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Authors: Fausto Morais

Abstract:

Artificial intelligence has been widely used in law. Programs are able to classify suits, to identify decision-making patterns, to predict outcomes, and to formalize legal arguments as well. In Brazil, the artificial intelligence victor has been classifying cases to supreme court’s standards. When those programs act doing those tasks, they simulate some kind of legal decision and legal arguments, raising doubts about how artificial intelligence can be integrated into legal reasoning. Taking this into account, the following three issues are identified; the problem of hypernormatization, the argument of legal anthropocentrism, and the artificial legal principles. Hypernormatization can be seen in the Brazilian legal context in the Supreme Court’s usage of the Victor program. This program generated efficiency and consistency. On the other hand, there is a feasible risk of over standardizing factual and normative legal features. Then legal clerks and programmers should work together to develop an adequate way to model legal language into computational code. If this is possible, intelligent programs may enact legal decisions in easy cases automatically cases, and, in this picture, the legal anthropocentrism argument takes place. Such an argument argues that just humans beings should enact legal decisions. This is so because human beings have a conscience, free will, and self unity. In spite of that, it is possible to argue against the anthropocentrism argument and to show how intelligent programs may work overcoming human beings' problems like misleading cognition, emotions, and lack of memory. In this way, intelligent machines could be able to pass legal decisions automatically by classification, as Victor in Brazil does, because they are binding by legal patterns and should not deviate from them. Notwithstanding, artificial intelligent programs can be helpful beyond easy cases. In hard cases, they are able to identify legal standards and legal arguments by using machine learning. For that, a dataset of legal decisions regarding a particular matter must be available, which is a reality in Brazilian Judiciary. Doing such procedure, artificial intelligent programs can support a human decision in hard cases, providing legal standards and arguments based on empirical evidence. Those legal features claim an argumentative weight in legal reasoning and should serve as references for judges when they must decide to maintain or overcome a legal standard.

Keywords: artificial intelligence, artificial legal principles, hypernormatization, legal anthropocentrism argument, legal reasoning

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Authors: Kwame Amoah

Abstract:

Artificial intelligence (AI) has the prospect of transforming project management, significantly improving efficiency and accuracy. By automating specific tasks with defined guidelines, AI can assist project managers in making better decisions and allocating resources efficiently, with possible risk mitigation. This study explores how AI is already impacting project management and likely future AI's impact on the field. The AI's reaction has been a divided opinion; while others picture it as a destroyer of jobs, some welcome it as an innovation advocate. Both sides agree that AI will be disruptive and revolutionize PM's functions. If current research is to go by, AI or some form will replace one-third of all learning graduate PM jobs by as early as 2030. A recent survey indicates AI spending will reach $97.9 billion by the end of 2023. Considering such a profound impact, the project management profession will also see a paradigm shift driven by AI. The study examines what the project management profession will look like in the next 5-10 years after this technological disruption. The research methods incorporate existing literature, develop trend analysis, and conduct structured interviews with project management stakeholders from North America to gauge the trend. PM professionals can harness the power of AI, ensuring a smooth transition and positive outcomes. AI adoption will maximize benefits, minimize adverse consequences, and uphold ethical standards, leading to improved project performance.

Keywords: project management, disruptive teacnologies, project management function, AL applications, artificial intelligence

Procedia PDF Downloads 83

Authors: Riona Dighe

Abstract:

This research attempts to identify an alternative approach to the measurement of Gross National Happiness (GNH). It uses artificial intelligence (AI), incorporating natural language processing (NLP) and sentiment analysis to measure GNH. We use ‘off the shelf’ NLP models responsible for the sentiment analysis of a sentence as a building block for this research. We constructed an algorithm using NLP models to derive a sentiment analysis score against sentences. This was then tested against a sample of 20 respondents to derive a sentiment analysis score. The scores generated resembled human responses. By utilising the MLP classifier, decision tree, linear model, and K-nearest neighbors, we were able to obtain a test accuracy of 89.97%, 54.63%, 52.13%, and 47.9%, respectively. This gave us the confidence to use the NLP models against sentences in websites to measure the GNH of a country.

Keywords: artificial intelligence, NLP, sentiment analysis, gross national happiness

Procedia PDF Downloads 119