The Use of Artificial Intelligence and Robotics -Related Films as a Tool to Introduce the Middle School Students to Artificial Intelligence and Robotics.

تساهم الأفلام القائمة على الذكاء الاصطناعي والروبوتات بشكل كبير في صناعة الأفلام، ومن الواضح أن أعدادها متناسبة مع أعمال السينما التي زادت بسرعة خلال العقد الماضي. وقد لوحظت هذه النسب المتزايدة منها بسبب التطور غير المسبوق في مفاهيم ومشاريع الذكاء الاصطناعي وتكنولوجيا الروبوتات التي اجتذبت طيفًا واسعًا من فئة المجتمعات. لذلك، يهدف هذا البحث إلى التحقق من تأثير هذا النوع من الأفلام على مجموعة من طلاب المرحلة الإعدادية. تم إعداد المنهجية المقترحة لهذه الدراسة لقياس الجوانب المختلفة لطلاب المرحلة الإعدادية على النحو التالي: (1) إدخال مثل هذه الموضوعات التكنولوجية و (2) زيادة مهارات الإبداع والتخيل. تم إجراء استبيان لتقييم الأثر على طلاب المدارس الإعدادية قبل وبعد مشاهدة مقاطع فيلم محددة مسبقًا تتعلق بالذكاء الاصطناعي والروبوتات. أظهرت نتائج الاستبيان أن مثل هذا النوع من الأفلام له آثار إيجابية أولية على دوافع الطلاب وإلهامهم وإبداعهم.Artificial intelligence and robotics-based films contribute significantly to the films industry, it is evidenced that it is proportion as part of the film business was increased rapidly during last decade. This was observed due to the increases in the artificial intelligence and robotics technology which have been attracting wide spectrum of the communities’ category. Therefore, this paper aim to investigate the impact of this type of films on the middle school students’ group. The proposed methodology of this study is made to measure various aspects for the middle school students as follows: (1) Introducing such technological topics and (2) Increasing the creativity and imagination skills. A survey to evaluate the impact on middle school students was conducted that consists of a list questionnaire before and after watching a pre-selected film clips related to artificial intelligence and robotics. It was evidenced that such as films type have initial positive effects on the students’ motivations, inspiration and creativity

Artificial Intelligence in Supply Chain Operations Planning: Collaboration and Digital Perspectives

[EN] Digital transformation provide supply chains (SCs) with extensive accurate data that should be combined with analytical techniques to improve their management. Among these techniques Artificial Intelligence (AI) has proved their suitability, memory and ability to manage uncertain and constantly changing information. Despite the fact that a number of AI literature reviews exist, no comprehensive review of reviews for the SC operations planning has yet been conducted. This paper aims to provide a comprehensive review of AI literature reviews in a structured manner to gain insights into their evolution in incorporating new ICTs and collaboration. Results show that hybrization man-machine and collaboration and ethical aspects are understudied.This research has been funded by the project entitled NIOTOME (Ref. RTI2018-102020-B-I00) (MCI/AEI/FEDER, UE). 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Applicability of artificial intelligence in e-commerce fashion platforms

A inovação tecnológica e a democratização da inteligência artificial (IA) têm vindo a alavancar o potencial de sucesso em todas as áreas que conhecemos hoje, com expectativas do que ainda está para vir. A presente dissertação propõe uma análise das aplicações da IA na indústria da moda, particularmente nas plataformas de marcas de moda do comércio eletrónico, e de que forma está a ter impacto na esfera pessoal do consumidor, particularmente no processo de tomada de decisão dos consumidores da Geração Z. O âmbito da IA tem vindo a evoluir de tal forma que permitiu às empresas não só melhorar a sua oferta e a procura dos clientes, como também proporcionar uma experiência de compra que vai para além da “seleção e compra” mecânica: os pontos de contacto impulsionados pela IA influenciam e enriquecem cada fase do processo de tomada de decisão, seja de forma mais positiva ou negativa. Em última análise, esta dissertação pretende proporcionar ao leitor um melhor conhecimento sobre a IA e o comércio eletrónico de moda, bem como delinear o seu impacto no comportamento online do consumidor.Technological innovation and democratization of artificial intelligence (AI) have been leveraging the potential success in every field we know today, while more is yet to come. The following dissertation proposes an analysis of AI achievements within the fashion industry, particularly in e-commerce fashion brand platforms, and how it is impacting the consumer personal sphere, particularly the decision-making process of Gen-Z consumers. The field of AI has been evolving in such a way that allows companies to not only improve their supply and customer demand, but also provide a shopping experience that goes beyond the mechanical “select and buy“: AI-driven touchpoints influence and enrich each stage of the decision-making process, whether more positively or negatively. Ultimately, this dissertation intends to provide the reader a better knowledge of AI and fashion e-commerce joining applications, and to delineate its impact on the online customer journey

Artificial intelligence in business-to-business marketing: a bibliometric analysis of current research status, development and future directions

Purpose-Although the value of AI has been acknowledged by companies, the literature shows challenges concerning AI-enabled B2B marketing innovation, as well as the diversity of roles AI can play in this regard. Accordingly, this study investigates the approaches that AI can be used for enabling B2B marketing innovation. Design/methodology/approach-Applying a bibliometric research method, this study systematically investigates the literature regarding AI-enabled B2B marketing. It synthesises state-of-the-art knowledge from 221 journal articles published between 1990 and 2021. Findings-Apart from offering specific information regarding the most influential authors and most frequently cited articles, the study further categorises the use of AI for innovation in B2B marketing into five domains, identified the main trends in the literature, and suggest directions for future research. Practical implications-Through our identified five domains, practitioners can assess their current use of AI ability in terms of their conceptualisation capability, technological applications, and identify their future needs in the relevant domains in order to make appropriate decisions on whether to invest in AI. Thus, the research outcomes can help companies to realise their digital marketing innovation strategy through AI. Originality/value-While more and more studies acknowledge the potential value of AI in B2B marketing, few attempts have been made to synthesise the literature. The results from the study can contribute by 1) obtaining and comparing the most influential works based on a series of analyses; 2) identifying five domains of research into how AI can be used for facilitating B2B marketing innovation; and 3) classifying relevant articles into five different time periods in order to identify both past trends and future directions in this specific field

A systematic literature review on machine learning applications for sustainable agriculture supply chain performance

Agriculture plays an important role in sustaining all human activities. Major challenges such as overpopulation, competition for resources poses a threat to the food security of the planet. In order to tackle the ever-increasing complex problems in agricultural production systems, advancements in smart farming and precision agriculture offers important tools to address agricultural sustainability challenges. Data analytics hold the key to ensure future food security, food safety, and ecological sustainability. Disruptive information and communication technologies such as machine learning, big data analytics, cloud computing, and blockchain can address several problems such as productivity and yield improvement, water conservation, ensuring soil and plant health, and enhance environmental stewardship. The current study presents a systematic review of machine learning (ML) applications in agricultural supply chains (ASCs). Ninety three research papers were reviewed based on the applications of different ML algorithms in different phases of the ASCs. The study highlights how ASCs can benefit from ML techniques and lead to ASC sustainability. Based on the study findings an ML applications framework for sustainable ASC is proposed. The framework identifies the role of ML algorithms in providing real-time analytic insights for pro-active data-driven decision-making in the ASCs and provides the researchers, practitioners, and policymakers with guidelines on the successful management of ASCs for improved agricultural productivity and sustainability

A Detailed Review of Artificial Intelligence Applied in the Fashion and Apparel Industry

The enormous impact of artificial intelligence has been realized in transforming the fashion and apparel industry in the past decades. However, the research in this domain is scattered and mainly focuses on one of the stages of the supply chain. Due to this, it is difficult to comprehend the work conducted in the distinct domain of the fashion and apparel industry. Therefore, this paper aims to study the impact and the significance of artificial intelligence in the fashion and apparel industry in the last decades throughout the supply chain. Following this objective, we performed a systematic literature review of research articles (journal and conference) associated with artificial intelligence in the fashion and apparel industry. Articles were retrieved from two popular databases ‘‘Scopus’’ and ‘‘Web of Science’’ and the article screening was completed in five phases resulting in 149 articles. This was followed by article categorization which was grounded on the proposed taxonomy and was completed in two steps. First, the research articles were categorized according to the artificial intelligence methods applied such as machine learning, expert systems, decision support system, optimization, and image recognition and computer vision. Second, the articles were categorized based on supply chain stages targeted such as design, fabric production, apparel production, and distribution. In addition, the supply chain stages were further classified based on business-to-business (B2B) and business-to-consumer (B2C) to give a broader outlook of the industry. As a result of the categorizations, research gaps were identified in the applications of AI techniques, at the supply chain stages and from a business (B2B/B2C) perspective. Based on these gaps, the future prospects of the AI in this domain are discussed. These can benefit the researchers in academics and industrial practitioners working in the domain of the fashion and apparel industry.Author 1 and 2 are equal contributing authors.</p