Search results for: organisational emotional intelligence

Authors: Robin C. Ladwig

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The future of work becomes less predictable, which requires increasing the adaptability of organizations to social and work changes. Society is transforming regarding gender identity in the sense that more people come forward to identify as trans and gender diverse (TGD). Organizations are ill-equipped to provide a safe and encouraging work environment by lacking inclusive organizational structures. The qualitative multi-method research about TGD inclusivity in the workplace explores the enablers and barriers for TGD individuals to satisfactory engage in the work environment and organizational culture. Furthermore, these TGD insights are analyzed about their organizational implications and awareness from a leadership and management perspective. The semi-structured online interviews with TGD individuals and the photo-elicit open-ended questionnaire addressed to leadership and management in diversity, career development, and human resources have been analyzed with a critical grounded theory approach. Findings demonstrated the significance of TGD voices, the support of leadership and management, as well as the synergy between voices and leadership. Hence, it indicates practical implications such as the revision of exclusive language used in policies, data collection, or communication and reconsideration of organizational decision-making by leaders to include TGD voices.

Keywords: future of work, occupational identity, organisational decision-making, trans and gender diverse identity

Procedia PDF Downloads 114

Authors: Pranav Ajmera, Amit Kharat, Tanveer Gupte, Richa Pant, Viraj Kulkarni, Vinay Duddalwar, Purnachandra Lamghare

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The cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR, that is, a value greater than 0.55, is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR from chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. We propose a deep learning-based model for automatic CTR calculation that can assist the radiologist with the diagnosis of cardiomegaly and optimize the radiology flow. The study population included 1012 posteroanterior (PA) CXRs from a single institution. The Attention U-Net deep learning (DL) architecture was used for the automatic calculation of CTR. A CTR of 0.55 was used as a cut-off to categorize the condition as cardiomegaly present or absent. An observer performance test was conducted to assess the radiologist's performance in diagnosing cardiomegaly with and without artificial intelligence (AI) assistance. The Attention U-Net model was highly specific in calculating the CTR. The model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. During the analysis, we observed that 51 out of 1012 samples were misclassified by the model when compared to annotations made by the expert radiologist. We further observed that the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Our segmentation-based AI model demonstrated high specificity and sensitivity for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows.

Keywords: cardiomegaly, deep learning, chest radiograph, artificial intelligence, cardiothoracic ratio

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Authors: Hsiao-Chuan Huang, King-Lung Kuo, Mei-Hsin Lo, Hsiao-Yun Chou, Yusen Lin

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The most robust and economical method for laboratory diagnosis of TB is to identify mycobacterial bacilli (AFB) under acid-fast staining despite its disadvantages of low sensitivity and labor-intensive. Though digital pathology becomes popular in medicine, an automated microscopic system for microbiology is still not available. A new AI-assisted automated microscopic system, consisting of a microscopic scanner and recognition program powered by big data and deep learning, may significantly increase the sensitivity of TB smear microscopy. Thus, the objective is to evaluate such an automatic system for the identification of AFB. A total of 5,930 smears was enrolled for this study. An intelligent microscope system (TB-Scan, Wellgen Medical, Taiwan) was used for microscopic image scanning and AFB detection. 272 AFB smears were used for transfer learning to increase the accuracy. Referee medical technicians were used as Gold Standard for result discrepancy. Results showed that, under a total of 1726 AFB smears, the automated system's accuracy, sensitivity and specificity were 95.6% (1,650/1,726), 87.7% (57/65), and 95.9% (1,593/1,661), respectively. Compared to culture, the sensitivity for human technicians was only 33.8% (38/142); however, the automated system can achieve 74.6% (106/142), which is significantly higher than human technicians, and this is the first of such an automated microscope system for TB smear testing in a controlled trial. This automated system could achieve higher TB smear sensitivity and laboratory efficiency and may complement molecular methods (eg. GeneXpert) to reduce the total cost for TB control. Furthermore, such an automated system is capable of remote access by the internet and can be deployed in the area with limited medical resources.

Keywords: TB smears, automated microscope, artificial intelligence, medical imaging

Procedia PDF Downloads 209

Authors: Anette Alén

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Intellectual property (IP) regimes have traditionally been designed to integrate various conflicting elements stemming from private entitlement and the public good. In IP laws and regulations, this design takes the form of specific uses of protected subject-matter without the right-holder’s consent, or exhaustion of exclusive rights upon market release, and the like. More recently, the pursuit of ‘balance’ has gained ground in the conceptualization of these conflicting elements both in terms of IP law and related policy. This can be seen, for example, in European Union (EU) copyright regime, where ‘balance’ has become a key element in argumentation, backed up by fundamental rights reasoning. This development also entails an ever-expanding dialogue between the IP regime and the constitutional safeguards for property, free speech, and privacy, among others. This study analyses the concept of ‘balance’ in EU copyright law: the research task is to examine the contents of the concept of ‘balance’ and the way it is operationalized and pursued, thereby producing new knowledge on the role and manifestations of ‘balance’ in recent copyright case law and regulatory instruments in the EU. The study discusses two particular pieces of legislation, the EU Digital Single Market (DSM) Copyright Directive (EU) 2019/790 and the finalized EU Artificial Intelligence (AI) Act, including some of the key preparatory materials, as well as EU Court of Justice (CJEU) case law pertaining to copyright in the digital era. The material is examined by means of document analysis, mapping the ways ‘balance’ is approached and conceptualized in the documents. Similarly, the interaction of fundamental rights as part of the balancing act is also analyzed. Doctrinal study of law is also employed in the analysis of legal sources. This study suggests that the pursuit of balance is, for its part, conducive to new battles, largely due to the advancement of digitalization and more recent developments in artificial intelligence. Indeed, the ‘balancing act’ rather presents itself as a way to bypass or even solidify some of the conflicting interests in a complex global digital economy. Indeed, such a conceptualization, especially when accompanied by non-critical or strategically driven fundamental rights argumentation, runs counter to the genuine acknowledgment of new types of conflicting interests in the copyright regime. Therefore, a more radical approach, including critical analysis of the normative basis and fundamental rights implications of the concept of ‘balance’, is required to readjust copyright law and regulations for the digital era. Notwithstanding the focus on executing the study in the context of the EU copyright regime, the results bear wider significance for the digital economy, especially due to the platform liability regime in the DSM Directive and with the AI Act including objectives of a ‘level playing field’ whereby compliance with EU copyright rules seems to be expected among system providers.

Keywords: balance, copyright, fundamental rights, platform liability, artificial intelligence

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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Authors: Jessica Kate Simonds

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The last successful hijacking perpetrated by Somali pirates in 2012 represented a critical turning point for the identity and brand of Indian Ocean (IO) insecurity, coined in this paper as the era of the post-piratical. This paper explores the broadening of the PMSC business model to account and contribute to the design of a new IO security environment that prioritises foreign and insurgency drone activity and Houthi rebel operations as the main threat to merchant shipping in the post-2012 era. This study is situated within a longer history of analysing maritime insecurity and also contributes a bespoke conceptual framework that understands the sea as a space that is produced and reproduced relative to existing and emerging threats to merchant shipping based on bespoke models of information sharing and intelligence acquisition. This paper also makes a prominent empirical contribution by drawing on a post-positivist methodology, data drawn from original semi-structured interviews with senior maritime insurers and active merchant seafarers that is triangulated with industry-produced guidance such as the BMP series as primary data sources. Each set is analysed through qualitative discourse and content analysis and supported by the quantitative data sets provided by the IMB Piracy Reporting center and intelligence networks. This analysis reveals that mechanisms such as the IGP&I Maritime Security Committee and intelligence divisions of PMSC’s have driven the exchanges of knowledge between land and sea and thus the reproduction of the maritime security environment through new regulations and guidance to account dones, rebels and bombs as the key challenges in the IO, beyond piracy. A contribution of this paper is the argument that experts who may not be in the highest-profile jobs are the architects of maritime insecurity based on their detailed knowledge and connections to vessels in transit. This paper shares the original insights of those who have served in critical decision making spaces to demonstrate that the development and refinement of industry produced deterrence guidance that has been accredited to the mitigation of piracy, have shaped new editions such as BMP 5 that now serve to frame a new security environment that prioritises the mitigation of risks from drones and WBEID’s from both state and insurgency risk groups. By highlighting the experiences and perspectives of key players on both land and at sea, the key finding of this paper is outlining that as pirates experienced a financial boom by profiteering from their bespoke business model during the peak of successful hijackings, the private security market encountered a similar level of financial success and guaranteed risk environment in which to prospect business. Thus, the reproduction of the Indian Ocean as a maritime security environment reflects a new found purpose for PMSC’s as part of the broader conglomerate of maritime insurers, regulators, shipowners and managers who continue to redirect the security consciousness and IO brand of insecurity.

Keywords: maritime security, private security, risk intelligence, political geography, international relations, political economy, maritime law, security studies

Procedia PDF Downloads 173

Authors: Weeam Abdulrahman, Gawaher Al-Madwary, Fatima Al-Ammari, Razan Mohammad

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This research discusses the AI features in Replika which is a dialog with a customized characters application, interaction and communication with AI in different ways that is provided for the user. spreading a survey with questions on how the AI worked is one approach of exposing the app to others to utilize and also we made an analysis that provides us with the conclusion of our research as a result, individuals will be able to try out the app. In the methodology we explain each page that pops up in the screen while using replika and Specify each part and icon.

Keywords: Replika, AI, artificial intelligence, dialog app

Procedia PDF Downloads 160

Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

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The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

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Authors: Barna Arnold Keserű

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In the last few years (what is called by many scholars the big data era) artificial intelligence (hereinafter AI) get more and more attention from the public and from the different branches of sciences as well. What previously was a mere science-fiction, now starts to become reality. AI and robotics often walk hand in hand, what changes not only the business and industrial life, but also has a serious impact on the legal system. The main research of the author focuses on these impacts in the field of private law, with special regards to liability and intellectual property issues. Many questions arise in these areas connecting to AI and robotics, where the boundaries are not sufficiently clear, and different needs are articulated by the different stakeholders. Recognizing the urgent need of thinking the Committee on Legal Affairs of the European Parliament adopted a Motion for a European Parliament Resolution A8-0005/2017 (of January 27th, 2017) in order to take some recommendations to the Commission on civil law rules on robotics and AI. This document defines some crucial usage of AI and/or robotics, e.g. the field of autonomous vehicles, the human job replacement in the industry or smart applications and machines. It aims to give recommendations to the safe and beneficial use of AI and robotics. However – as the document says – there are no legal provisions that specifically apply to robotics or AI in IP law, but that existing legal regimes and doctrines can be readily applied to robotics, although some aspects appear to call for specific consideration, calls on the Commission to support a horizontal and technologically neutral approach to intellectual property applicable to the various sectors in which robotics could be employed. AI can generate some content what worth copyright protection, but the question came up: who is the author, and the owner of copyright? The AI itself can’t be deemed author because it would mean that it is legally equal with the human persons. But there is the programmer who created the basic code of the AI, or the undertaking who sells the AI as a product, or the user who gives the inputs to the AI in order to create something new. Or AI generated contents are so far from humans, that there isn’t any human author, so these contents belong to public domain. The same questions could be asked connecting to patents. The research aims to answer these questions within the current legal framework and tries to enlighten future possibilities to adapt these frames to the socio-economical needs. In this part, the proper license agreements in the multilevel-chain from the programmer to the end-user become very important, because AI is an intellectual property in itself what creates further intellectual property. This could collide with data-protection and property rules as well. The problems are similar in the field of liability. We can use different existing forms of liability in the case when AI or AI led robotics cause damages, but it is unsure that the result complies with economical and developmental interests.

Keywords: artificial intelligence, intellectual property, liability, robotics

Procedia PDF Downloads 189

Authors: Xinyu Wang

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In the context of the rapid development of Internet technology and the increasing prevalence of online social media, this study investigates the impact of digital communication on social learning. Through three behavioral experiments, we explore both affective and cognitive social learning in online environments. Experiment 1 manipulates the content of experimental materials and two forms of feedback, emotional valence, sociability, and repetition, to verify whether individuals can achieve online emotional social learning through reinforcement using two social learning strategies. Results reveal that both social learning strategies can assist individuals in affective, social learning through reinforcement, with feedback-based learning strategies outperforming frequency-dependent strategies. Experiment 2 similarly manipulates the content of experimental materials and two forms of feedback to verify whether individuals can achieve online knowledge social learning through reinforcement using two social learning strategies. Results show that similar to online affective social learning, individuals adopt both social learning strategies to achieve cognitive social learning through reinforcement, with feedback-based learning strategies outperforming frequency-dependent strategies. Experiment 3 simultaneously observes online affective and cognitive social learning by manipulating the content of experimental materials and feedback at different levels of social pressure. Results indicate that online affective social learning exhibits different learning effects under different levels of social pressure, whereas online cognitive social learning remains unaffected by social pressure, demonstrating more stable learning effects. Additionally, to explore the sustained effects of online social learning and differences in duration among different types of online social learning, all three experiments incorporate two test time points. Results reveal significant differences in pre-post-test scores for online social learning in Experiments 2 and 3, whereas differences are less apparent in Experiment 1. To accurately measure the sustained effects of online social learning, the researchers conducted a mini-meta-analysis of all effect sizes of online social learning duration. Results indicate that although the overall effect size is small, the effect of online social learning weakens over time.

Keywords: online social learning, affective social learning, cognitive social learning, social learning strategies, social reinforcement, social pressure, duration

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Authors: Kolawole Taiwo Olabode

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This is a comparative study of ICTs adoption and use in selected manufacturing industries in for development. This study was carried out 2004 and was repeated 2013 (nine years after) using the same selected manufacturing industries to assess the level, improvement and extent ICT facilities used in these companies. The theory of modernization was explored to explain some developmental issues in this study. The same semi-structured questionnaire and IDI were used to elicit data on the subject matter. About 24.9% of the total workers (1,247) were sampled for this study using quota sampling technique. SPSS was used to analysis the quantitative data. The qualitative data was used to buttress the quantitative data. Findings indicated that Seven-Up Bottling Company and Frigoglass Glass Industry still remained Intensive ICT Users while only Niger Match Nigeria Limited still remained Non-Intensive ICT User while unfortunately, Askar Paint Nigeria Limited has gone liquidated. It is also important to discover that only the Intensive ICT users improved on relevant ICT facilities. The existing problems of ICT adoption and used in these companies remained the same in Niger Match Limited. The study concluded that for a society to be developed, management and government at all levels must do all things necessary to ensure that all existing organisations must be ICT compliance for workers and organisational performance and to enhance nation’s development in order to compete with other companies for global standard or recognition.

Keywords: ICT, intensive ICT-users, entrepreneurial, manufacturing industries, industries and development

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Authors: Swati Bajpai, S. P. K Jena

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Background: There is dramatic increase in the prevalence and severity of overweight in adolescents, raising concerns about their psychosocial and cognitive consequences, thereby indicating the immediate need to understand the effects of increased weight on scholastic performance. Although the body of research is currently limited, available results have identified an inverse relationship between obesity and cognition in adolescents. Aim: to examine the association between increased Body Mass Index in adolescents and their neurocognitive functioning. Methods: A case –control study of 28 subjects in the age group of 11-17 years (14 Males and 14 females) was taken on the basis of main inclusion criteria (Body Mass Index). All of them were randomized to (experimental group: overweight) and (control group: normal weighted). A complete neurocognitive assessment was carried out using validated psychological scales namely, Color Progressive Matrices (to assess intelligence); Bender Visual Motor Gestalt Test (Perceptual motor functioning); PGI-Memory Scale for Children (memory functioning) and Malin’s Intelligence Scale Indian Children (verbal and performance ability). Results: statistical analysis of the results depicted that 57% of the experimental group lack in cognitive abilities, especially in general knowledge (99.1±12.0 vs. 102.8±6.7), working memory (91.5±8.4 vs. 93.1±8.7), concrete ability (82.3±11.5 vs. 92.6±1.7) and perceptual motor functioning (1.5±1.0 vs. 0.3±0.9) as compared to control group. Conclusion: Our investigations suggest that weight gain results, at least in part, from a neurological predisposition characterized by reduced executive function, and in turn obesity itself has a compounding negative impact on the brain. Though, larger sample is needed to make more affirmative claims.

Keywords: adolescents, body mass index, neurocognition, obesity

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Authors: Loris Franchi, Daniele Calvi, Sabrina Corpino

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This paper presents a design methodology in which stakeholders are assisted with the exploration of a so-called negotiation space, aiming to the maximization of both group social welfare and single stakeholder’s perceived utility. The outcome results in less design iterations needed for design convergence while obtaining a higher solution effectiveness. During the early stage of a space project, not only the knowledge about the system but also the decision outcomes often are unknown. The scenario is exacerbated by the fact that decisions taken in this stage imply delayed costs associated with them. Hence, it is necessary to have a clear definition of the problem under analysis, especially in the initial definition. This can be obtained thanks to a robust generation and exploration of design alternatives. This process must consider that design usually involves various individuals, who take decisions affecting one another. An effective coordination among these decision-makers is critical. Finding mutual agreement solution will reduce the iterations involved in the design process. To handle this scenario, the paper proposes a design methodology which, aims to speed-up the process of pushing the mission’s concept maturity level. This push up is obtained thanks to a guided negotiation space exploration, which involves autonomously exploration and optimization of trade opportunities among stakeholders via Artificial Intelligence algorithms. The negotiation space is generated via a multidisciplinary collaborative optimization method, infused by game theory and multi-attribute utility theory. In particular, game theory is able to model the negotiation process to reach the equilibria among stakeholder needs. Because of the huge dimension of the negotiation space, a collaborative optimization framework with evolutionary algorithm has been integrated in order to guide the game process to efficiently and rapidly searching for the Pareto equilibria among stakeholders. At last, the concept of utility constituted the mechanism to bridge the language barrier between experts of different backgrounds and differing needs, using the elicited and modeled needs to evaluate a multitude of alternatives. To highlight the benefits of the proposed methodology, the paper presents the design of a CubeSat mission for the observation of lunar radiation environment. The derived solution results able to balance all stakeholders needs and guaranteeing the effectiveness of the selection mission concept thanks to its robustness in valuable changeability. The benefits provided by the proposed design methodology are highlighted, and further development proposed.

Keywords: concurrent engineering, artificial intelligence, negotiation in engineering design, multidisciplinary optimization

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Authors: Ahmad Haidar

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The rapid adoption of Artificial Intelligence (AI) technology holds unforeseen risks like privacy violation, unemployment, and algorithmic bias, triggering research institutions, governments, and companies to develop principles of AI ethics. The extensive and diverse literature on AI lacks an analysis of the evolution of principles developed in recent years. There are two fundamental purposes of this paper. The first is to provide insights into how the principles of AI ethics have been changed recently, including concepts like risk management and public participation. In doing so, a NOISE (Needs, Opportunities, Improvements, Strengths, & Exceptions) analysis will be presented. Second, offering a framework for building Ethical AI linked to sustainability. This research adopts an explorative approach, more specifically, an inductive approach to address the theoretical gap. Consequently, this paper tracks the different efforts to have “trustworthy AI” and “ethical AI,” concluding a list of 12 documents released from 2017 to 2022. The analysis of this list unifies the different approaches toward trustworthy AI in two steps. First, splitting the principles into two categories, technical and net benefit, and second, testing the frequency of each principle, providing the different technical principles that may be useful for stakeholders considering the lifecycle of AI, or what is known as sustainable AI. Sustainable AI is the third wave of AI ethics and a movement to drive change throughout the entire lifecycle of AI products (i.e., idea generation, training, re-tuning, implementation, and governance) in the direction of greater ecological integrity and social fairness. In this vein, results suggest transparency, privacy, fairness, safety, autonomy, and accountability as recommended technical principles to include in the lifecycle of AI. Another contribution is to capture the different basis that aid the process of AI for sustainability (e.g., towards sustainable development goals). The results indicate data governance, do no harm, human well-being, and risk management as crucial AI for sustainability principles. This study’s last contribution clarifies how the principles evolved. To illustrate, in 2018, the Montreal declaration mentioned eight principles well-being, autonomy, privacy, solidarity, democratic participation, equity, and diversity. In 2021, notions emerged from the European Commission proposal, including public trust, public participation, scientific integrity, risk assessment, flexibility, benefit and cost, and interagency coordination. The study design will strengthen the validity of previous studies. Yet, we advance knowledge in trustworthy AI by considering recent documents, linking principles with sustainable AI and AI for sustainability, and shedding light on the evolution of guidelines over time.

Keywords: artificial intelligence, AI for sustainability, declarations, framework, regulations, risks, sustainable AI

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Authors: Khulud Alhussain, Anna Gavine, Stephen Macgillivray, Sushila Chowdhry

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Background: Estimates show that by the year 2030, mental illness will account for more than half of the global economic burden, second to non-communicable diseases. Often, the perinatal period is characterised by psychological ambivalence and a mixed anxiety-depressive condition. Maternal mental disorder is associated with perinatal anxiety and depression and affects breastfeeding behaviors. Studies also indicate that maternal mental health can considerably influence a baby's health in numerous aspects and impact the newborn health due to lack of adequate breastfeeding. However, studies reporting factors associated with breastfeeding behaviors are predominantly quantitative. Therefore, it is not clear what literature is available to understand the factors affecting breastfeeding and perinatal women’s perspectives and experiences. Aim: This review aimed to explore the perceptions and experiences of women with perinatal anxiety and depression, as well as how these experiences influence their breastfeeding behaviours. Methods: A systematic literature review of qualitative studies in line with the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ). Four electronic databases (CINAHL, PsycINFO, Embase, and Google Scholar) were explored for relevant studies using a search strategy. The search was restricted to studies published in the English language between 2000 and 2022. Findings from the literature were screened using a pre-defined screening criterion and the quality of eligible studies was appraised using the Walsh and Downe (2006) checklist. Findings were extracted and synthesised based on Braun and Clark. The review protocol was registered on PROSPERO (Ref: CRD42022319609). Result: A total of 4947 studies were identified from the four databases. Following duplicate removal and screening 16 studies met the inclusion criteria. The studies included 87 pregnant and 302 post-partum women from 12 countries. The participants were from a variety of economic, regional, and religious backgrounds, mainly from the age of 18 to 45 years old. Three main themes were identified: Barriers to breastfeeding, breastfeeding facilitators, emotional disturbance, and breastfeeding. Seven subthemes emerged from the data: expectation versus reality, uncertainly about maternal competencies, body image and breastfeeding, lack of sufficient breastfeeding support for family and caregivers’ support, influences positive breastfeeding practices, breastfeeding education, and causes of mental strain among breastfeeding women. Breastfeeding duration is affected in women with mental health disorders, irrespective of their desire to breastfeed. Conclusion: There is significant empirical evidence that breastfeeding behaviour and perinatal mental disturbance are linked. However, there is a lack of evidence to apply the findings to Saudi women due to lack of empirical qualitative information. To improve the psychological well-being of mothers, it is crucial to explore and recognise any concerns with their mental, physical, and emotional well-being. Therefore, robust research is needed so that breastfeeding intervention researchers and policymakers can focus on specifically what needs to be done to help mentally distressed perinatal women and their new-born.

Keywords: pregnancy, perinatal period, anxiety, depression, emotional disturbance, breastfeeding

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Authors: Siddhartha Khare, Nitish Kr Boro, Omm Animesh Mishra

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Recent food price hikes may signal the end of an era of predictable global grain crop plenty due to climate change, population expansion, and dietary changes. Food consumption will treble in 20 years, requiring enormous production expenditures. Climate and the atmosphere changed owing to rainfall and seasonal cycles in the past decade. India's tropical agricultural relies on evapotranspiration and monsoons. In places with limited resources, the global environmental change affects agricultural productivity and farmers' capacity to adjust to changing moisture patterns. Motivated by these difficulties, satellite remote sensing might be combined with near-surface imaging data (smartphones, UAVs, and PhenoCams) to enable phenological monitoring and fast evaluations of field-level consequences of extreme weather events on smallholder agriculture output. To accomplish this technique, we must digitally map all communities agricultural boundaries and crop kinds. With the improvement of satellite remote sensing technologies, a geo-referenced database may be created for rural Indian agriculture fields. Using AI, we can design digital agricultural solutions for individual farms. Main objective is to Geo-enable each farm along with their seasonal crop information by combining Artificial Intelligence (AI) with satellite and near-surface data and then prepare long term crop monitoring through in-depth field analysis and scanning of fields with satellite derived vegetation indices. We developed an AI based algorithm to understand the timelapse based growth of vegetation using PhenoCam or Smartphone based images. We developed an android platform where user can collect images of their fields based on the android application. These images will be sent to our local server, and then further AI based processing will be done at our server. We are creating digital boundaries of individual farms and connecting these farms with our smart phone application to collect information about farmers and their crops in each season. We are extracting satellite-based information for each farm from Google earth engine APIs and merging this data with our data of tested crops from our app according to their farm’s locations and create a database which will provide the data of quality of crops from their location.

Keywords: artificial intelligence, satellite remote sensing, crop monitoring, android and web application

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Authors: Mona Maria Mombeck

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Social participation in heterogeneous classrooms is one of the main goals in inclusive education. Children with special educational needs (SEN) and children with learning difficulties, or behavioural problems not diagnosed as SEN, are more likely to be excluded by other children than others. It is proven that the presence of dogs, as well as contact with dogs, increases the likelihood of positive social behaviour between humans. Therefore, animal-assisted pedagogy may be presumed to be a constructive way of inclusive teaching and facing the challenges of social inclusion in school classes. This study investigates the presence of a friendly dog in heterogeneous groups of pupils in order to evaluate the influence of dogs on facets of social participation of children in school. 30 German pupils, aged from 10 to 14, in four classes were questioned about their social participation before and after they were educated for a year in school with animal-assisted-pedagogy, using the problem-concerned interview method. In addition, the post-interview includes some general questions about the putative differences or similarities of being educated with and without a dog. The interviews were analysed with the qualitative-content-analysis using QDA software. The results showed that a dog has a positive impact on the atmosphere, student relationships, and well-being in class. Regarding the atmosphere, the pupils mainly argued that the improvement was caused by taking into account the dog’s well-being, respecting the dog-related rules, and by emotional self-regulation. It can be supposed that children regard the rules concerning the dog as more relevant to them than rules, not concerning the dog even if they require the same behaviour and goal. Furthermore, a dog has a positive impact on emotional self-regulation and, therefore, on pupil’s behaviour in class and the atmosphere. In terms of the statements about relationships, the dog’s presence was mainly seen to provide both a unifying aim and a uniting topic to talk about. The improved well-being was described as a feeling of joy and peace of mind. Moreover, the teacher was evaluated as more friendly and trustworthy after animal-assisted teaching. Nevertheless, animal-assisted pedagogy can, rarely, cause problems as well, such as jealousy, distraction, or concerns about the well-being of the dog. The study could prove the relevance of animal-assisted pedagogy for facing the challenges of social participation in inclusive education.

Keywords: animal-assisted-pedagogy, inclusive education, human-animal-interactions, social participation

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Authors: Wendy Maddison

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This paper describes an Action Research project which was undertaken to inform professional practice in order to develop a newly created Centre for Student Success in the specific context of transnational medical and nursing education in the Middle East. The objectives were to enhance the academic performance, persistence, integration and personal and professional development of a multinational study body, in particular in relation to preclinical medical students, and to establish a comfortable, friendly and student-driven environment within an Irish medical university recently established in Bahrain. Expatriating a new part of itself into a corner of the world and within a context which could be perceived as the antithesis of itself, in particular in terms of traditional cultural and organisational values, the university has had to innovate in the range of services, programmes and other offerings which engages and supports the academic success of medical and nursing students as they “encounter the world in the classroom” in the context of an Arab Islamic culture but within a European institution of transnational education, engaging with a global learning environment locally. The outcomes of the project resulted in the development of a specific student success ‘signature’ for this particular transnational higher education context.

Keywords: transnational higher education, medical education, action research, student success, Middle Eastern context, student persistence in the global-local, student support mechanisms

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Authors: Rasha Alhendawi

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This research will give the introductory ideas for cultural adaption of B2C E-Service design in Germany. By the intense competition of E-Service development, many companies have realized the importance of understanding the emotional and cultural characteristics of their customers. Ignoring customers’ needs and requirements throughout the E-Service design can lead to faults, mistakes, and gaps. The term of E-Service usability now is changed not only to develop high quality E-Services, but also to be extended to include customer satisfaction and provide for them to feel local.

Keywords: human computer interaction (HCI), usability, cultural usability, E-Services, business-to-consumer (B2C), e-services

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Authors: Hacer Kobya Bulut, Kıymet Yeşilçiçek Çalık, Birsel Canan Demirbağ, Hacer Erdöl, Songül Aktaş

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Introduction: As an expression of an emotion which always exists in life, crying is regarded as one of the problematic behaviors of patients by nurses. Towards such patients, nurses may exhibit emotional and behavioral reactions such as feeling helpless, anger, indifferent, defense, and opposition. However crying either meets a need, reduces the tension to cope with problems or helps patient to gain strength. Therefore, nurses must accept that crying is a normal mechanism that reduces emotional tension and should approach a crying patient accordingly. Objective: This study was carried out to evaluate the communicational behaviors of the nurses towards ‘crying patient’. Methods: This descriptive study was conducted with the nurses working at a university hospital in a city in the Eastern Black Sea in June-September 2015. The entire universe was tried to be reached without sampling. 90% of the population was reached and the study was completed with 309 nurses who volunteered to participate in the study. Data were collected through a questionnaire which was prepared reviewing the literature by researchers. Data were evaluated in SPSS analysis program using percentages, numbers and chi-square test with the 95% confidence interval and p <0.05significance level. Findings: The findings showed that the average age of nurses was 31.52 ± 7.96, work experience was 10:09 ± 7.69 and only 22.7% had training about ‘approach to crying patient’ during their education. 97.1% of the nurses often faced with crying patients in their professional lives, 62.8% stated that they faced crying women patients. When they see crying patients, 84.8% of the nurses ‘do not want the patient to cry’, 80.9% wonder ‘why they are crying’, % 79.6 ‘feel uneasiness’,% 79.3 ‘feel sorry’ and 41.4% ‘ feel helpless’. The question ‘Why do you think the patient is crying?’ was answered by 93.5% nurses as ‘they are suffering’, by 86.1% ‘they are helpless’, 80.9% ‘they are sad’, 79.6% ‘they need help’, 54.4% ‘because they feel inadequate,’ and 44.7% ‘they fail to control their crying behavior. ‘How do you approach to your patient when she/he is crying?’ question was answered by 82.5% of nurses as ‘I would console’, 77.3% as ‘I would ask the reason’, 63.1% as ‘I would try to stop her from crying’ all of which are actually inappropriate nursing approaches. However, 92.2% of the nurses stated that ‘I do not judge the crying patient’, ‘87.1% said ‘I allocate time to crying patients’ and 85.8% said ‘ I ask patient whether they want to cry alone’. The study showed that educational background and work experience of the nurses affected the appropriate approach to crying patients (P <0.05). Conclusion: As a result of the study, it was found out that nurses do not want patients to cry, so they exhibit inappropriate approach such as consoling the patients and they have difficulty in approaching crying patients.

Keywords: approach to patient, communication, crying patient, nurse, Turkey

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Authors: Vahid Bairami Rad

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The intelligentization of agricultural fields can control the temperature, humidity, and variables affecting the growth of agricultural products online and on a mobile phone or computer. Smarting agricultural fields and gardens is one of the best and best ways to optimize agricultural equipment and has a 100 percent direct effect on the growth of plants and agricultural products and farms. Smart farms are the topic that we are going to discuss today, the Internet of Things and artificial intelligence. Agriculture is becoming smarter every day. From large industrial operations to individuals growing organic produce locally, technology is at the forefront of reducing costs, improving results and ensuring optimal delivery to market. A key element to having a smart agriculture is the use of useful data. Modern farmers have more tools to collect intelligent data than in previous years. Data related to soil chemistry also allows people to make informed decisions about fertilizing farmland. Moisture meter sensors and accurate irrigation controllers have made the irrigation processes to be optimized and at the same time reduce the cost of water consumption. Drones can apply pesticides precisely on the desired point. Automated harvesting machines navigate crop fields based on position and capacity sensors. The list goes on. Almost any process related to agriculture can use sensors that collect data to optimize existing processes and make informed decisions. The Internet of Things (IoT) is at the center of this great transformation. Internet of Things hardware has grown and developed rapidly to provide low-cost sensors for people's needs. These sensors are embedded in IoT devices with a battery and can be evaluated over the years and have access to a low-power and cost-effective mobile network. IoT device management platforms have also evolved rapidly and can now be used securely and manage existing devices at scale. IoT cloud services also provide a set of application enablement services that can be easily used by developers and allow them to build application business logic. Focus on yourself. These development processes have created powerful and new applications in the field of Internet of Things, and these programs can be used in various industries such as agriculture and building smart farms. But the question is, what makes today's farms truly smart farms? Let us put this question in another way. When will the technologies associated with smart farms reach the point where the range of intelligence they provide can exceed the intelligence of experienced and professional farmers?

Keywords: food security, IoT automation, wireless communication, hybrid lifestyle, arduino Uno

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Authors: Samuel Quashie

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PhDs in Construction Management often designs their methods based on those established in social sciences using theoretical models, to collect, gather and analysis data to answer research questions. Work aim is to apply qualitative and quantitative as a data analysis method, and as part of the theoretical framework - descriptive theory. To improve the ability to replicate the contribution to knowledge the research. Using practical triangulation approach, which covers, interviews and observations, literature review and (archival) document studies, project-based case studies, questionnaires surveys and review of integrated systems used in, construction and construction related industries. The clarification of organisational context and management delivery that influences organizational performance and quality of product and measures are achieved. Results illustrate improved reliability in this research approach when interpreting real world phenomena; cumulative results of research can be applied with confidence under similar environments. Assisted validity of the PhD research outcomes and strengthens the confidence to apply cumulative results of research under similar conditions in the Built Environment research systems, which have been criticised for the lack of reliability in approaches when interpreting real world phenomena.

Keywords: case studies, descriptive theory, theoretical framework, qualitative and quantitative research

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Authors: Fiffy Hanisdah Saikim, Bruce Prideaux

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One of the aims of wildlife tourism is to educate visitors about the threats facing wildlife, in general, and the actions needed to protect the environment and maintain biodiversity. Annually, millions of tourists visit natural areas and zoos primarily to view flagship species such as rhinos and elephants. Venues rely on the inherent charisma of these species to increase visitation and anchor conservation efforts. Expected visitor outcomes from the use of flagships include raised levels of awareness and pro-conservation behaviors. However, the role of flagships in wildlife tourism has been criticized for not delivering conservation benefits for species of interest or biodiversity and producing negative site impacts. Furthermore, little is known about how the connection to a species influences conservation behaviors. This paper addresses this gap in knowledge by extending previous work exploring wildlife tourism to include the emotional connection formed with wildlife species and pro-conservation behaviors for individual species and biodiversity. This paper represents a substantial contribution to the field because (a) it incorporates the role of the experience in understanding how tourists connect with a species and how this connection influences pro-conservation behaviors; and (b) is the first attempt to operationalize Conservation Caring as a measure of tourists’ connection with a species. Existing studies have investigated how specific elements, such as interpretation or species’ morphology may influence programmatic goals or awareness. However, awareness is a poor measure of an emotional connection with an animal. Furthermore, there has not been work done to address the holistic nature of the wildlife viewing experience, and its subsequent influence on behaviors. Results based on the structural equation modelling, support the validity of Conservation Caring as a factor; the ability of wildlife tourism to influence Conservation Caring; and that this connection is a strong predictor of conservation awareness behaviors. These findings suggest wildlife tourism can deliver conservation outcomes. The studies in this paper also provide a valuable framework for structuring wildlife tourism experiences to align with flagship related conservation outcomes, and exploring a wider assemblage of species as potential flagships.

Keywords: wildlife tourism, conservation caring, conservation awareness, structural equation modelling

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Authors: Josephine Eghonghon Ahiante, Barry Hanyane

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To give effect to the constitutional rights of gender equality, the South African government instituted various legislative policy frameworks and legislations to equalise the public service. Nonetheless, gender inequality in senior management positions remains a rift in government institutions, particularly the local sphere of government. The methodology for gathering and analysing data for this study was based on both primary and secondary data sources, namely literature review, qualitative and quantitative data collection and analysis, triangulation, and inductive and deductive thematic analysis. The study found that misogynist tendencies which are manifest in organisational culture suffocate the good intentions of government in ensuring social justices, leadership diversity, and women equality. It also demonstrates that traditional gender role expectation still informs the ground in which senior management positions are allocated, men perceive women as non-leadership fit and discriminate against them during recruitment, selection, and promotion into high positions. The analyses from the study portray that, while government legislation and framework has been instrumental in the leadership acceleration of women, much more has to be done to deconstruct internalised leadership stereotypes on women's gender roles and leadership requirements. The study recommends that gender bias training intervention is needed to teach public employees on management excellence.

Keywords: gender, leadership, misogyny, orgnisational cultural, patriachy

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Authors: Mikel Alonso López

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The early eighties saw the rise of a new research trend in several prestigious journals, mainly articles that related emotions with the decision-making processes of the consumer, and stopped treating them as external elements. That is why we ask questions such as: what are emotions? Are there different types of emotions? What components do they have? Which theories exist about them? In this study, we will review the main theories and components of emotion analysing the cognitive factor and the different emotional states that are generally recognizable with a focus in the classic debate as to whether they occur before the cognitive process or the affective process.

Keywords: emotion, consumer behaviour, feelings, decision making

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Authors: Anup Anand Deshmukh, Catherine Soladie, Renaud Seguier

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Emotion plays a key role in many applications like healthcare, to gather patients’ emotional behavior. Unlike typical ASR (Automated Speech Recognition) problems which focus on 'what was said', it is equally important to understand 'how it was said.' There are certain emotions which are given more importance due to their effectiveness in understanding human feelings. In this paper, we propose an approach that models human stress from audio signals. The research challenge in speech emotion detection is finding the appropriate set of acoustic features corresponding to an emotion. Another difficulty lies in defining the very meaning of emotion and being able to categorize it in a precise manner. Supervised Machine Learning models, including state of the art Deep Learning classification methods, rely on the availability of clean and labelled data. One of the problems in affective computation is the limited amount of annotated data. The existing labelled emotions datasets are highly subjective to the perception of the annotator. We address the first issue of feature selection by exploiting the use of traditional MFCC (Mel-Frequency Cepstral Coefficients) features in Convolutional Neural Network. Our proposed Emo-CNN (Emotion-CNN) architecture treats speech representations in a manner similar to how CNN’s treat images in a vision problem. Our experiments show that Emo-CNN consistently and significantly outperforms the popular existing methods over multiple datasets. It achieves 90.2% categorical accuracy on the Emo-DB dataset. We claim that Emo-CNN is robust to speaker variations and environmental distortions. The proposed approach achieves 85.5% speaker-dependant categorical accuracy for SAVEE (Surrey Audio-Visual Expressed Emotion) dataset, beating the existing CNN based approach by 10.2%. To tackle the second problem of subjectivity in stress labels, we use Lovheim’s cube, which is a 3-dimensional projection of emotions. Monoamine neurotransmitters are a type of chemical messengers in the brain that transmits signals on perceiving emotions. The cube aims at explaining the relationship between these neurotransmitters and the positions of emotions in 3D space. The learnt emotion representations from the Emo-CNN are mapped to the cube using three component PCA (Principal Component Analysis) which is then used to model human stress. This proposed approach not only circumvents the need for labelled stress data but also complies with the psychological theory of emotions given by Lovheim’s cube. We believe that this work is the first step towards creating a connection between Artificial Intelligence and the chemistry of human emotions.

Keywords: deep learning, brain chemistry, emotion perception, Lovheim's cube

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Authors: Paula C. Ferreira, Ana Margarida Veiga Simão, Nádia Pereira, Aristides Ferreira, Alexandra Marques Pinto, Alexandra Barros, Vitor Martinho

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Cyberbullying is a phenomenon of worldwide concern regarding children and adolescents’ mental health and risk behavior. Bystanders of this phenomenon can help diminish the incidence of this phenomenon if they engage in pro-social behavior. However, different social-cognitive and affective bystander reactions may surface because of the lack of contextual information and emotional cues in cyberbullying situations. Hence, this study investigated how cyberbullying bystanders’ unpleasant emotions could be related to their personal moral beliefs and their behavioral intentions to cyberbully or defend the victim. It also proposed to investigate how their normative beliefs of perceived severity about cyberbullying behavior could be related to their personal moral beliefs and their behavioral intentions. Three groups of adolescents participated in this study, namely a first of group 402 students (5th – 12th graders; Mage = 13.12; SD = 2.19; 55.7% girls) to compute explorative factorial analyses of the instruments used; a second group of 676 students (5th – 12th graders; Mage = 14.10; SD = 2.74; 55.5% were boys) to run confirmatory factor analyses; and a third group (N = 397; 5th – 12th graders; Mage = 13.88 years; SD = 1.45; 55.5% girls) to perform the main analyses to test the research hypotheses. Self-report measures were used, such as the Personal moral beliefs about cyberbullying behavior questionnaire, the Normative beliefs of perceived severity about cyberbullying behavior questionnaire, the Unpleasant emotions about cyberbullying incidents questionnaires, and the Bystanders’ behavioral intentions in cyberbullying situations questionnaires. Path analysis results revealed that unpleasant emotions were mediators of the relationship between adolescent cyberbullying bystanders’ personal moral beliefs and their intentions to help the victims in cyberbullying situations. Moreover, adolescent cyberbullying bystanders’ normative beliefs of gravity were mediators of the relationship between their personal moral beliefs and their intentions to cyberbully others. These findings provide insights for the development of prevention and intervention programs that promote social and emotional learning strategies as a means to prevent and intervene in cyberbullying.

Keywords: cyberbullying, normative beliefs of perceived severity, personal moral beliefs, unpleasant emotions

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Authors: Ivan Traina, Luigi Sangalli, Fabio Tognon, Angelo Lascioli

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Currently, challenges regarding preschooler children are mainly focused on a sedentary lifestyle. Also, motor activity in infancy is seen as a tool for the separate acquisition of cognitive and socio-emotional skills rather than considering neuromotor development as a tool for improving learning abilities. The paper utilized an observational research method to shed light on the results of practicing neuromotor exercises in preschool children with disability as well as provide implications for practice.

Keywords: children with disability, learning abilities, inclusion, neuromotor development

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Authors: M. Gil, R. Montalvo

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Business Intelligence (BI) has become a major source of competitive advantages for firms around the world. BI has been defined as the process of data visualization and reporting for understanding what happened and what is happening. Moreover, BI has been studied for its predictive capabilities in the context of trade and financial transactions. The current literature has identified that BI permits managers to identify market trends, understand customer relations, and predict demand for their products and services. This last capability of BI has been of special concern to academics. Specifically, due to its power to build predictive models adaptable to specific time horizons and geographical regions. However, the current literature of BI focuses on predicting specific markets and industries because the impact of such predictive models was relevant to specific industries or organizations. Currently, the existing literature has not developed a predictive model of BI that takes into consideration the whole economy of a geographical area. This paper seeks to create a predictive model of BI that would show the bigger picture of a geographical area. This paper uses a data set from the Secretary of Economic Development of the state of Jalisco, Mexico. Such data set includes data from all the commercial transactions that occurred in the state in the last years. By analyzing such data set, it will be possible to generate a BI model that predicts supply and demand from specific industries around the state of Jalisco. This research has at least three contributions. Firstly, a methodological contribution to the BI literature by generating the predictive supply and demand model. Secondly, a theoretical contribution to BI current understanding. The model presented in this paper incorporates the whole picture of the economic field instead of focusing on a specific industry. Lastly, a practical contribution might be relevant to local governments that seek to improve their economic performance by implementing BI in their policy planning.

Keywords: business intelligence, predictive model, supply and demand, Mexico

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Authors: Mokhlisur Rahman

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Ocean inhibits one of the most complex ecosystems on the planet that regulates the earth's climate and weather by providing us with compatible weather to live. Ocean provides food by extending various ways of lifestyles that are dependent on it, transportation by accommodating the world's biggest carriers, recreation by offering its beauty in many moods, and home to countless species. At the essence of receiving various forms of entertainment, consumers choose to be close to the ocean while performing many fun activities. Which, at some point, upsets the stomach of the ocean by threatening marine life and the environment. Consumers throw the waste into the ocean after using it. Most of them are plastics that float over the ocean and turn into thousands of micro pieces that are hard to observe with the naked eye but easily eaten by the sea species. Eventually, that conflicts with the natural consumption process of any living species, making them sick. This information is not known by most consumers who go to the sea or seashores occasionally to spend time, nor is it widely discussed, which creates an information gap among consumers. However, advertising is a powerful tool to educate people about ocean pollution. This abstract analyzes three major ocean-saving advertisement campaigns that use innovative and advanced technology to get maximum exposure. The study collects data from the selected campaigns' websites and retrieves all available content related to messages, videos, and images. First, the SeaLegacy campaign uses stunning images to create awareness among the people; they use social media content, videos, and other educational content. They create content and strategies to build an emotional connection among the consumers that encourage them to move on an action. All the messages in their campaign empower consumers by using powerful words. Second, Ocean Conservancy Campaign uses social media marketing, events, and educational content to protect the ocean from various pollutants, including plastics, climate change, and overfishing. They use powerful images and videos of marine life. Their mission is to create evidence-based solutions toward a healthy ocean. Their message includes the message regarding the local communities along with the sea species. Third, ocean clean-up is a campaign that applies strategies using innovative technologies to remove plastic waste from the ocean. They use social media, digital, and email marketing to reach people and raise awareness. They also use images and videos to evoke an emotional response to take action. These tree advertisements use realistic images, powerful words, and the presence of living species in the imagery presentation, which are eye-catching and can grow emotional connection among the consumers. Identifying the effectiveness of the messages these advertisements carry and their strategies highlights the knowledge gap of mass people between real pollution and its consequences, making the message more accessible to the mass of people. This study aims to provide insights into the effectiveness of ocean-saving advertisement campaigns and their impact on the public's awareness of ocean conservation. The findings from this study help shape future campaigns.

Keywords: advertising-campaign, content-creation, images ocean-saving technology, videos

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