Search results for: moral intelligence

Authors: Adane Kassa Shikur

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Today’s supply chains (SC) have become vulnerable to unexpected and ever-intensifying disruptions from myriad sources. Consequently, the concept of supply chain resilience (SCRes) has become crucial to complement the conventional risk management paradigm, which has failed to cope with unexpected SC disruptions, resulting in severe consequences affecting SC performances and making business continuity questionable. Advancements in cutting-edge technologies like artificial intelligence (AI) and their potential to enhance SCRes by improving critical antecedents in the different phases have attracted the attention of scholars and practitioners. The research from academia and the practical interest of the industry have yielded significant publications at the nexus of AI and SCRes during the last two decades. However, the applications and examinations have been primarily conducted independently, and the extant literature is dispersed into research streams despite the complex nature of SCRes. To close this research gap, this study conducts a systematic literature review of 106 peer-reviewed articles by curating, synthesizing, and consolidating up-to-date literature and presents the state-of-the-art development from 2010 to 2022. Bayesian networks are the most topical ones among the 13 AI techniques evaluated. Concerning the critical antecedents, visibility is the first ranking to be realized by the techniques. The study revealed that AI techniques support only the first 3 phases of SCRes (readiness, response, and recovery), and readiness is the most popular one, while no evidence has been found for the growth phase. The study proposed an AI-SCRes framework to inform research and practice to approach SCRes holistically. It also provided implications for practice, policy, and theory as well as gaps for impactful future research.

Keywords: ANNs, risk, Bauesian networks, vulnerability, resilience

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Authors: Gordon James Knowles

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Historically the Chinatown district in Sandwich Islands has been plagued with the traditional vice crimes of illegal drugs, gambling, and prostitution since the early 1800s. However, a new form of psychologically addictive arcade style table gambling machines has become the dominant form of illegal revenue made in Honolulu, Hawaii. This study attempts to document the drive, desire, or will to play and wager with arcade style video gaming and understand the role of illegal game rooms in facilitating pathological gambling addiction. Indicators of police corruption by Chinese organized crime syndicates related to protection rackets, bribery, and pay-offs were revealed. Information fusion from a police science and sociological intelligence perspective indicates insurgent warfare is being waged on the streets of Honolulu by the People’s Republic of China. This state-sponsored communist terrorism in the Hawaiian Islands used “contactless” irregular warfare entailing: (1) the deployment of psychologically addictive gambling machines, (2) the distribution of the physically addictive fentanyl drug as a lethal chemical weapon, and (3) psychological warfare by circulating pro-China anti-American propaganda newspapers targeted at the small island populace.

Keywords: Chinese and Polynesian organized crime, china daily newspaper, electronic arcade style table games, gaming technology addiction, illegal compulsive gambling, and police intelligence

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Authors: Syed Qasim Gilani, Muhammad Umair, Muhammad Noman, Syed Bilawal Shah, Aqib Abbasi, Muhammad Waheed

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Cardiovascular Diseases(CVD's) are the major cause of death in the world. The main reason for these deaths is the unavailability of first aid for heart failure. In many cases, patients die before reaching the hospital. We in this paper are presenting innovative online health service for Cardiac Patients. The proposed online health system has two ends. Users through device developed by us can communicate with their doctor through a mobile application. This interface provides them with first aid.Also by using this service, they have an easy interface with their doctors for attaining medical advice. According to the proposed system, we developed a device called Cardiac Care. Cardiac Care is a portable device which a patient can use at their home for monitoring heart condition. When a patient checks his/her heart condition, Electrocardiogram (ECG), Blood Pressure(BP), Temperature are sent to the central database. The severity of patients condition is checked using Artificial Intelligence Algorithm at the database. If the patient is suffering from the minor problem, our algorithm will suggest a prescription for patients. But if patient's condition is severe, patients record is sent to doctor through the mobile Android application. Doctor after reviewing patients condition suggests next step. If a doctor identifies the patient condition as critical, then the message is sent to the central database for sending an ambulance for the patient. Ambulance starts moving towards patient for bringing him/her to hospital. We have implemented this model at prototype level. This model will be life-saving for millions of people around the globe. According to this proposed model patients will be in contact with their doctors all the time.

Keywords: cardiovascular disease, classification, electrocardiogram, blood pressure

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Authors: Elina Vroblevska, Toms Ratfelders

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This paper examines the impact of international status-seeking aspirations on great power behavior within the international system. In particular, we seek to test the assumption advanced by the proponents of the Social Identity Theory (SIT) that the inability to achieve social mobilization through joining perceived higher-status social groups (of states) leads great powers to adopt the approach of social competition in which they aim to equal or outdo the dominant group in the area on which its claim to superior status rests. Since the dissolution of the Soviet Union, Russia has struggled to be accepted as a great power by the group of Western states that had created the dominant international system order, while the Soviet states were isolated. While the 1990s and the beginning of the 21st century can be characterized by striving to integrate into the existing order, the second decade has seen a rather sharp turn towards creating a new power center for Russia through the realization of ideas of multipolarity rivalry and uniqueness of the state itself. Increasingly, we have seen the Kremlin striving to collaborate and mobilize groups of states that fall outside of the categories of democracy, multiculturalism, and international order, the way that is perceived by the dominant group, which can be described as the West. Instead, Russia builds its own narrative where it creates an alternative understanding of these values, differentiating from the higher-status social group. The Global South, from a Russian perspective, is the group of states that can still be swayed to create an alternative power center in the international system - one where Russia can assert its status as a great power. This is based on a number of reasons, the most important being that the global north is already highly institutionalized in terms of economy (the EU) and defense (NATO), leaving no room for Russia but to integrate within the existing framework. Second, the difference in values and their interpretation - Russia has been adamant, for the last twenty years, on basing its moral code on traditional values like religion, the heterosexual family model, and moral superiority, which contradict the overall secularism of the Global North. And last, the striking difference in understanding of state governance models - with Russia becoming more autocratic over the course of the last 20 years, it has deliberately created distance between itself and democratic states, entering a “gray area” of alternative understanding of democracy which is more relatable to the global South countries. Using computational text analysis of the excerpts of Vladimir Putin’s speeches delivered from 2000-2022 regarding the areas that fall outside the immediate area of interest of Russia (the Global South), we identify 80 topics that relate to the particular component of the great power status - interest to use force globally. These topics are compared across four temporal frames that capture the periods of more and less permissible Western social boundaries. We find that there exists a negative association between such permissiveness and Putin’s emphasis on the “use of force” topics. This lends further support to the Social Identity Theory and contributes to broadening its applicability to explaining the questions related to great power assertiveness in areas outside of their primary focus regions.

Keywords: Russia, Global South, great power, identity

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Authors: Ahmed Hossam ElMolla, Mohamed Hatem Saleh, Hamza Mostafa, Lara Mamdouh, Yassin Wael

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Artificial intelligence (AI) is reshaping industries, and its potential to revolutionize renewable energy and data center operations is immense. By harnessing AI's capabilities, we can optimize energy consumption, predict fluctuations in renewable energy generation, and improve the efficiency of data center infrastructure. This convergence of technologies promises a future where energy is managed more intelligently, sustainably, and cost-effectively. The integration of AI into renewable energy systems unlocks a wealth of opportunities. Machine learning algorithms can analyze vast amounts of data to forecast weather patterns, solar irradiance, and wind speeds, enabling more accurate energy production planning. AI-powered systems can optimize energy storage and grid management, ensuring a stable power supply even during intermittent renewable generation. Moreover, AI can identify maintenance needs for renewable energy infrastructure, preventing costly breakdowns and maximizing system lifespan. Data centers, which consume substantial amounts of energy, are prime candidates for AI-driven optimization. AI can analyze energy consumption patterns, identify inefficiencies, and recommend adjustments to cooling systems, server utilization, and power distribution. Predictive maintenance using AI can prevent equipment failures, reducing energy waste and downtime. Additionally, AI can optimize data placement and retrieval, minimizing energy consumption associated with data transfer. As AI transforms renewable energy and data center operations, modified Key Performance Indicators (KPIs) will emerge. Traditional metrics like energy efficiency and cost-per-megawatt-hour will continue to be relevant, but additional KPIs focused on AI's impact will be essential. These might include AI-driven cost savings, predictive accuracy of energy generation and consumption, and the reduction of carbon emissions attributed to AI-optimized operations. By tracking these KPIs, organizations can measure the success of their AI initiatives and identify areas for improvement. Ultimately, the synergy between AI, renewable energy, and data centers holds the potential to create a more sustainable and resilient future. By embracing these technologies, we can build smarter, greener, and more efficient systems that benefit both the environment and the economy.

Keywords: data center, artificial intelligence, renewable energy, energy efficiency, sustainability, optimization, predictive analytics, energy consumption, energy storage, grid management, data center optimization, key performance indicators, carbon emissions, resiliency

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Authors: David Edgar

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To understand how the brain’s subconscious and conscious functions, we must conquer the physics of Unity, which leads to duality’s algorithm. Where the subconscious (bottom-up) and conscious (top-down) processes function together to produce and consume intelligence, we use terms like ‘time is relative,’ but we really do understand the meaning. In the brain, there are different processes and, therefore, different observers. These different processes experience time at different rates. A sensory system such as the eyes cycles measurement around 33 milliseconds, the conscious process of the frontal lobe cycles at 300 milliseconds, and the subconscious process of the thalamus cycle at 5 milliseconds. Three different observers experience time differently. To bridge observers, the thalamus, which is the fastest of the processes, maintains a synchronous state and entangles the different components of the brain’s physical process. The entanglements form a synchronous cohesion between the brain components allowing them to share the same state and execute in the same measurement cycle. The thalamus uses the shared state to control the firing sequence of the brain’s linear subconscious process. Sharing state also allows the brain to cheat on the amount of sensory data that must be exchanged between components. Only unpredictable motion is transferred through the synchronous state because predictable motion already exists in the shared framework. The brain’s synchronous subconscious process is entirely based on energy conservation, where prediction regulates energy usage. So, the eyes every 33 milliseconds dump their sensory data into the thalamus every day. The thalamus is going to perform a motion measurement to identify the unpredictable motion in the sensory data. Here is the trick. The thalamus conducts its measurement based on the original observation time of the sensory system (33 ms), not its own process time (5 ms). This creates a data payload of synchronous motion that preserves the original sensory observation. Basically, a frozen moment in time (Flat 4D). The single moment in time can then be processed through the single state maintained by the synchronous process. Other processes, such as consciousness (300 ms), can interface with the synchronous state to generate awareness of that moment. Now, synchronous data traveling through a separate faster synchronous process creates a theoretical time tunnel where observation time is tunneled through the synchronous process and is reproduced on the other side in the original time-relativity. The synchronous process eliminates time dilation by simply removing itself from the equation so that its own process time does not alter the experience. To the original observer, the measurement appears to be instantaneous, but in the thalamus, a linear subconscious process generating sensory perception and thought production is being executed. It is all just occurring in the time available because other observation times are slower than thalamic measurement time. For life to exist in the physical universe requires a linear measurement process, it just hides by operating at a faster time relativity. What’s interesting is time dilation is not the problem; it’s the solution. Einstein said there was no universal time.

Keywords: neural synchronization, natural intelligence, 99.95% IoT data transmission savings, artificial subconscious intelligence (ASI)

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Authors: Sophaphan Intahphuak, Narong Pamala, Boonyaporn Yodkhong, Samuhavitayaa

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The amount of solid waste increases each year as a result of population growth, urbanization and economic expansion; however, there was little public cooperation in the segregation of solid waste due to the lack of awareness. This study aims to encourage all sectors in the community to participate in the development of a suitable model to reduce environmental waste by emerging the cultural context that bares a close relationship with Buddhism through faith and merit-making. The monks, involving stakeholder in the entire waste management system, help publicize the campaign on Buddhist holy days, religious ceremonies and they also teach people to be responsible for the garbage problem in the community. As for the garbage brought for merit-making, they are sold and the money is used to help build the pavilion. It was found that people can separate recycled garbage and the amount of solid waste slightly decrease. The results obtained suggest that the religion is not only the moral center of the community, it is also the center of community empowerment to consciousness in waste management.

Keywords: community empowerment, religion’s role, waste management, recycled garbage

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Authors: Younis R. Elhaddad, Mohamed A. Alshaari

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Since 2700 B.C the problem of constructing magic squares attracts many researchers. Magic squares one of most difficult challenges for mathematicians. In this work, we describe how to construct and enumerate Pan- magic squares using genetic algorithm, using new chromosome encoding technique. The results were promising within reasonable time.

Keywords: genetic algorithm, magic square, pan-magic square, computational intelligence

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Authors: Halimat M. Ajose-Adeogun, Zaynab A. Bello

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Artificial Intelligence (AI) and Natural Language Processing (NLP) have transformed computer language comprehension, allowing computers to comprehend spoken and written language with human-like cognition. NLP, a multidisciplinary area that combines rule-based linguistics, machine learning, and deep learning, enables computers to analyze and comprehend human language. NLP applications in medicine range from tackling issues in electronic health records (EHR) and psychiatry to improving diagnostic precision in orthopedic surgery and optimizing clinical procedures with novel technologies like chatbots. The technology shows promise in a variety of medical sectors, including quicker access to medical records, faster decision-making for healthcare personnel, diagnosing dysplasia in Barrett's esophagus, boosting radiology report quality, and so on. However, successful adoption requires training for healthcare workers, fostering a deep understanding of NLP components, and highlighting the significance of validation before actual application. Despite prevailing challenges, continuous multidisciplinary research and collaboration are critical for overcoming restrictions and paving the way for the revolutionary integration of NLP into medical practice. This integration has the potential to improve patient care, research outcomes, and administrative efficiency. The research methodology includes using NLP techniques for Sentiment Analysis and Emotion Recognition, such as evaluating text or audio data to determine the sentiment and emotional nuances communicated by users, which is essential for designing a responsive and sympathetic chatbot. Furthermore, the project includes the adoption of a Personalized Intervention strategy, in which chatbots are designed to personalize responses by merging NLP algorithms with specific user profiles, treatment history, and emotional states. The synergy between NLP and personalized medicine principles is critical for tailoring chatbot interactions to each user's demands and conditions, hence increasing the efficacy of mental health care. A detailed survey corroborated this synergy, revealing a remarkable 20% increase in patient satisfaction levels and a 30% reduction in workloads for healthcare practitioners. The poll, which focused on health outcomes and was administered to both patients and healthcare professionals, highlights the improved efficiency and favorable influence on the broader healthcare ecosystem.

Keywords: natural language processing, artificial intelligence, healthcare communication, electronic health records, patient care

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Authors: Jacquelyn Burkell, Jane Bailey

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Artificial intelligence (AI) solutions deployed within the justice system face the critical task of providing acceptable explanations for decisions or actions. These explanations must satisfy the joint criteria of public and professional accountability, taking into account the perspectives and requirements of multiple stakeholders, including judges, lawyers, parties, witnesses, and the general public. This research project analyzes and integrates two existing literature on explanations in order to propose guidelines for explainable AI in the justice system. Specifically, we review three bodies of literature: (i) explanations of the purpose and function of 'explainable AI'; (ii) the relevant case law, judicial commentary and legal literature focused on the form and function of reasons for judicial decisions; and (iii) the literature focused on the psychological and sociological functions of these reasons for judicial decisions from the perspective of the public. Our research suggests that while judicial ‘reasons’ (arguably accurate descriptions of the decision-making process and factors) do serve similar explanatory functions as those identified in the literature on 'explainable AI', they also serve an important ‘justification’ function (post hoc constructions that justify the decision that was reached). Further, members of the public are also looking for both justification and explanation in reasons for judicial decisions, and that the absence of either feature is likely to contribute to diminished public confidence in the legal system. Therefore, artificially automated judicial decision-making systems that simply attempt to document the process of decision-making are unlikely in many cases to be useful to and accepted within the justice system. Instead, these systems should focus on the post-hoc articulation of principles and precedents that support the decision or action, especially in cases where legal subjects’ fundamental rights and liberties are at stake.

Keywords: explainable AI, judicial reasons, public accountability, explanation, justification

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Authors: Yahaya Sulaiman, Ibrahim Muhammad Yakuba, Abubakar Usman

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In this paper, we discuss that Sustainable Development Goals are in consonance with Islamic ethos and philosophy. Islam made emphasize on human well-being from spiritual, physiological, intellectual and economic perspectives. Islamic worldview and values framework strengthens moral consciousness, urge pro-social behaviour and engender environmental ethics which can help in influencing our attitudes towards meeting sustainable development challenges. Islamic social finance institutions like Zakat and Waqf can contribute towards scaling up efforts in commercially non-viable, but socially vital projects and programs. There is much potential for Islamic finance to promote sustainable economic development through such approaches as widening access to finance, financing infrastructure projects, and expanding the reach of Takaful. Real sector based productive enterprise in Islamic finance has positive implications for the ecosystem. Risk-sharing shifts the emphasis from credit-worthiness of the borrower to be placed on the value creation and economic viability of investments that create new wealth. Islamic social finance package can cater to the financially excluded households.

Keywords: assessment, Islamic, economic, sustainable, development

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Authors: Stephen M. Magu

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The 9/11 and Christmas bombing attacks in the United States are mostly associated with the inability of intelligence agencies to connect dots based on intelligence that was already available. The 1998, 2002, 2013 and several 2014 terrorist attacks in Kenya, on the other hand, are probably driven by a completely different dynamic: the invisible hand of corruption. The World Bank and Transparency International annually compute the Worldwide Governance Indicators and the Corruption Perception Index respectively. What perhaps is not adequately captured in the corruption metrics is the impact of corruption on terrorism. The World Bank data includes variables such as the control of corruption, (estimates of) government effectiveness, political stability and absence of violence/terrorism, regulatory quality, rule of law and voice and accountability. TI's CPI does not include measures related to terrorism, but it is plausible that there is an expectation of some terrorism impact arising from corruption. This paper, by examining the incidence, frequency and total number of terrorist attacks that have occurred especially since 1990, and further examining the specific cases of Kenya and Nigeria, argues that in addition to having major effects on governance, corruption has an even more frightening impact: that of facilitating and/or violating security mechanisms to the extent that foreign nationals can easily obtain identification that enables them to perpetuate major events, targeting powerful countries' interests in countries with weak corruption-fighting mechanisms. The paper aims to model interactions that demonstrate the cost/benefit analysis and agents' rational calculations as being non-rational calculations, given the ultimate impact. It argues that eradication of corruption is not just a matter of a better business environment, but that it is implicit in national security, and that for anti-corruption crusaders, this is an argument more potent than the economic cost / cost of doing business argument.

Keywords: corruption, global south, identification, passports, terrorism

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Authors: Mohammad Iqbal Malik

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Economic development of a country depends on its business ethics and values. Islamic teachings provide obvious guidelines in this regard. Mutual understanding, honesty and ethics possess central place in Islamic teachings. These teachings not only prohibit from amalgamation, black-marketing, hoarding and deception but also force to treat a customer amiably. Business ethics are broad spectrum in Islamic society due to these high moral values. One can estimate the importance of business ethics in Islam for economic development in Islamic society is evident by the Hadith of Prophet Muhammad (PBUH) in which He excluded a trader from Muslim community who did not behave in good manners with his clients. In this study we present impact of business ethics on economic development of a Muslim society. This study is based on basic teachings of Quran and Sunnah, Fiqh, Economics and other books. This study explores the behavior of Muslim traders and reveals useful inferences. Policy makers, traders, academia, scholars, and general public may learn lessons from this study with respect to business ethics in Islamic perspective.

Keywords: ethics, business, Islam, economic perspective

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Authors: Vanja Skoric

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The paper examines whether the impacts of artificial intelligence (AI) can be meaningfully addressed through the rights-based approach to AI design, investigating in particular how the inclusive, participatory process of assessing the AI impact would make this viable. There is a significant gap between envisioning rights-based AI systems and their practical application. Plausibly, internalizing human rights approach within AI design process might be achieved through identifying and assessing implications of AI features human rights, especially considering the case of vulnerable individuals and communities. However, there is no clarity or consensus on how such an instrument should be operationalised to usefully identify the impact, mitigate harms and meaningfully ensure relevant stakeholders’ participation. In practice, ensuring the meaningful inclusion of those individuals, groups, or entire communities who are affected by the use of the AI system is a prerequisite for a process seeking to assess human rights impacts and risks. Engagement in the entire process of the impact assessment should enable those affected and interested to access information and better understand the technology, product, or service and resulting impacts, but also to learn about their rights and the respective obligations and responsibilities of developers and deployers to protect and/or respect these rights. This paper will provide an overview of the study and practice of the participatory design process for AI, including inclusive impact assessment, its main elements, propose a framework, and discuss the lessons learned from the existing theory. In addition, it will explore pathways for enhancing and promoting individual and group rights through such engagement by discussing when, how, and whom to include, at which stage of the process, and what are the pre-requisites for meaningful and engaging. The overall aim is to ensure using the technology that works for the benefit of society, individuals, and particular (historically marginalised) groups.

Keywords: rights-based design, AI impact assessment, inclusion, harm mitigation

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Authors: Sylvester Akpah, Selasi Vondee

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Unmanned Aerial Vehicles (UAV) as they are also known, drones have attracted increasing attention in recent years due to their ubiquitous nature and boundless applications in the areas of communication, surveying, aerial photography, weather forecasting, medical delivery, surveillance amongst others. Operated remotely in real-time or pre-programmed, drones can fly autonomously or on pre-defined routes. The application of these aerial vehicles has successfully penetrated the world due to technological evolution, thus a lot more businesses are utilizing their capabilities. Unfortunately, while drones are replete with the benefits stated supra, they are riddled with some problems, mainly attributed to the complexities in learning how to master drone flights, collision avoidance and enterprise security. Additional challenges, such as the analysis of flight data recorded by sensors attached to the drone may take time and require expert help to analyse and understand. This paper presents an autonomous drone control system using a chatbot. The system allows for easy control of drones using conversations with the aid of Natural Language Processing, thus to reduce the workload needed to set up, deploy, control, and monitor drone flight missions. The results obtained at the end of the study revealed that the drone connected to the chatbot was able to initiate flight missions with just text and voice commands, enable conversation and give real-time feedback from data and requests made to the chatbot. The results further revealed that the system was able to process natural language and produced human-like conversational abilities using Artificial Intelligence (Natural Language Understanding). It is recommended that radio signal adapters be used instead of wireless connections thus to increase the range of communication with the aerial vehicle.

Keywords: artificial ntelligence, chatbot, natural language processing, unmanned aerial vehicle

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Authors: Noha El Toukhy

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The ‘beauty is good” stereotype suggests that people perceive attractive people as having several positive characteristics. Likewise, an “anomalous-is-bad” stereotype is hypothesized to facilitate biases against people with anomalous or less attractive faces. Researchers integrated both into a stereotype content model, which is one of the frameworks used in this study to assess how facial anomalies influence people’s social attitudes and, specifically, people’s ratings of warmth and competence. The mind perception theory, as well as the assessment of animalistic and mechanistic dehumanization against facially anomalous people, are two further frameworks that we are using in this study. This study will test the hypothesis that people have negative attitudes towards people with facial anomalies. We also hypothesize that people have negative biases toward faces with visible differences compared to faces without such differences regardless of the specific type of anomaly, as well as that individual differences in psychological dispositions bear on the expression of the anomalous-is-bad stereotype. Using highly controlled and some never-before-used face stimuli, this pre-registered study examines whether moral character influences perceptions of attractiveness, warmth, and competence for facial anomalies.

Keywords: adolescents, attractiveness, competence, social attitudes, warmth

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Authors: Aleksandra Czubak

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The need to present evidence in civil proceedings is essential for getting the right result. It is for this reason that it is particularly important for the parties to present the most relevant and convincing evidence to the Court. Therefore, parties often try to gain evidence, even when the acquisition of such evidence is in breach of the law. Firstly, there will be discussed how evidence is applied in the Polish civil process and the Polish regulations of the evidence proceedings; with specific reference to evidence of major importance in the developing world. Further, it will be discussed the controversies connected with the admission of illegally gained evidence in civil proceedings. The credibility of the various measures is circumstantial and can only be determined by factors related to the recognized problem. For that reason, it is not the amount of evidence, but the value and relevance of this evidence that should be considered in determining the right result. This paper will also consider whether the end justifies the means? How far should parties go in order to achieve a favorable sentence or to create stronger evidence? Methods of persuasion of the court, as well as the acquisition of evidence, are not always fair and moral. It is on this area of controversy that this essay will focus. This paper concludes by considering the value of evidence and the possibility of using it to achieve a just sentence. Examples are based on Polish law; nevertheless, they encompass ideas common to most civil jurisdictions.

Keywords: civil proceedings, Europe (Poland), evidence, law

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Authors: Magdi Elmessiry, Adel Elmessiry

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The fashion industry represents a significant portion of the global gross domestic product, however, it is plagued by cheap imitators that infringe on the trademarks which destroys the fashion industry's hard work and investment. While eventually the copycats would be found and stopped, the damage has already been done, sales are missed and direct and indirect jobs are lost. The infringer thrives on two main facts: the time it takes to discover them and the lack of tracking technologies that can help the consumer distinguish them. Blockchain technology is a new emerging technology that provides a distributed encrypted immutable and fault resistant ledger. Blockchain presents a ripe technology to resolve the infringement epidemic facing the fashion industry. The significance of the study is that a new approach leveraging the state of the art blockchain technology coupled with artificial intelligence is used to create a framework addressing the fashion infringement problem. It transforms the current focus on legal enforcement, which is difficult at best, to consumer awareness that is far more effective. The framework, Crypto CopyCat, creates an immutable digital asset representing the actual product to empower the customer with a near real time query system. This combination emphasizes the consumer's awareness and appreciation of the product's authenticity, while provides real time feedback to the producer regarding the fake replicas. The main findings of this study are that implementing this approach can delay the fake product penetration of the original product market, thus allowing the original product the time to take advantage of the market. The shift in the fake adoption results in reduced returns, which impedes the copycat market and moves the emphasis to the original product innovation.

Keywords: fashion, infringement, blockchain, artificial intelligence, textiles supply chain

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Authors: Jamerson Felipe Pereira Lima, Jeane Cecília Bezerra de Melo

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The difficulty and cost related to obtaining of protein tertiary structure information through experimental methods, such as X-ray crystallography or NMR spectroscopy, helped raising the development of computational methods to do so. An approach used in these last is prediction of tridimensional structure based in the residue chain, however, this has been proved an NP-hard problem, due to the complexity of this process, explained by the Levinthal paradox. An alternative solution is the prediction of intermediary structures, such as the secondary structure of the protein. Artificial Intelligence methods, such as Bayesian statistics, artificial neural networks (ANN), support vector machines (SVM), among others, were used to predict protein secondary structure. Due to its good results, artificial neural networks have been used as a standard method to predict protein secondary structure. Recent published methods that use this technique, in general, achieved a Q3 accuracy between 75% and 83%, whereas the theoretical accuracy limit for protein prediction is 88%. Alternatively, to achieve better results, support vector machines prediction methods have been developed. The statistical evaluation of methods that use different AI techniques, such as ANNs and SVMs, for example, is not a trivial problem, since different training sets, validation techniques, as well as other variables can influence the behavior of a prediction method. In this study, we propose a prediction method based on artificial neural networks, which is then compared with a selected SVM method. The chosen SVM protein secondary structure prediction method is the one proposed by Huang in his work Extracting Physico chemical Features to Predict Protein Secondary Structure (2013). The developed ANN method has the same training and testing process that was used by Huang to validate his method, which comprises the use of the CB513 protein data set and three-fold cross-validation, so that the comparative analysis of the results can be made comparing directly the statistical results of each method.

Keywords: artificial neural networks, protein secondary structure, protein structure prediction, support vector machines

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Authors: Jongtae Rhee, Sangkwon Han, Seungbin Ji, Junhyeong Park, Byeonghun Kim, Taekyung Kim, Byeonghyeon Jeon, Jiwoo Yang

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The shared kitchen is a concept that transfers the value of the sharing economy to the kitchen. It is a type of kitchen equipped with cooking facilities that allows multiple companies or chefs to share time and space and use it jointly. These shared kitchens provide economic benefits and convenience, such as reduced investment costs and rent, but also increase the risk of safety management, such as cross-contamination of food ingredients. Therefore, to manage the safety of food ingredients and finished products in a shared kitchen where several entities jointly use the kitchen and handle various types of food ingredients, it is critical to manage followings: the freshness of food ingredients, user hygiene and safety and cross-contamination of cooking equipment and facilities. In this study, it propose a machine learning-based system for hygiene safety and cross-contamination management, which are highly difficult to manage. User clothing management and user access management, which are most relevant to the hygiene and safety of shared kitchens, are solved through machine learning-based methodology, and cutting board usage management, which is most relevant to cross-contamination management, is implemented as an integrated safety management system based on artificial intelligence. First, to prevent cross-contamination of food ingredients, we use images collected through a real-time camera to determine whether the food ingredients match a given cutting board based on a real-time object detection model, YOLO v7. To manage the hygiene of user clothing, we use a camera-based facial recognition model to recognize the user, and real-time object detection model to determine whether a sanitary hat and mask are worn. In addition, to manage access for users qualified to enter the shared kitchen, we utilize machine learning based signature recognition module. By comparing the pairwise distance between the contract signature and the signature at the time of entrance to the shared kitchen, access permission is determined through a pre-trained signature verification model. These machine learning-based safety management tasks are integrated into a single information system, and each result is managed in an integrated database. Through this, users are warned of safety dangers through the tablet PC installed in the shared kitchen, and managers can track the cause of the sanitary and safety accidents. As a result of system integration analysis, real-time safety management services can be continuously provided by artificial intelligence, and machine learning-based methodologies are used for integrated safety management of shared kitchens that allows dynamic contracts among various users. By solving this problem, we were able to secure the feasibility and safety of the shared kitchen business.

Keywords: artificial intelligence, food safety, information system, safety management, shared kitchen

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Authors: Andrew D. Henshaw, James M. Austin

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Introduction: Human trafficking is an increasingly serious transnational criminal enterprise and social security issue. Despite ongoing efforts to mitigate the phenomenon and a significant expansion of security scrutiny over past decades, it is not receding. This is true for many nations in Southeast Asia, widely recognized as the global hub for trafficked persons, including men, women, and children. Clearly, human trafficking is difficult to address because there are numerous drivers, causes, and motivators for it to persist, such as non-military and non-traditional security challenges, i.e., climate change, global warming displacement, and natural disasters. These make displaced persons and refugees particularly vulnerable. The issue is so large conservative estimates put a dollar value at around $150 billion-plus per year (Niethammer, 2020) spanning sexual slavery and exploitation, forced labor, construction, mining and in conflict roles, and forced marriages of girls and women. Coupled with corruption throughout military, police, and civil authorities around the world, and the active hands of powerful transnational criminal organizations, it is likely that such figures are grossly underestimated as human trafficking is misreported, under-detected, and deliberately obfuscated to protect those profiting from it. For example, the 2022 UN report on human trafficking shows a 56% reduction in convictions in that year alone (UNODC, 2022). Our Approach: To better understand this, our research utilizes a bespoke methodology. Applying a JAM (Juxtaposition Assessment Matrix), which we previously developed to detect flows of dark money around the globe (Henshaw, A & Austin, J, 2021), we now focus on the human trafficking paradigm. Indeed, utilizing a JAM methodology has identified key indicators of human trafficking not previously explored in depth. Being a set of structured analytical techniques that provide panoramic interpretations of the subject matter, this iteration of the JAM further incorporates behavioral and driver indicators, including the employment of Open-Source Artificial Intelligence (OS-AI) across multiple collection points. The extracted behavioral data was then applied to identify non-traditional indicators as they contribute to human trafficking. Furthermore, as the JAM OS-AI analyses data from the inverted position, i.e., the viewpoint of the traffickers, it examines the behavioral and physical traits required to succeed. This transposed examination of the requirements of success delivers potential leverage points for exploitation in the fight against human trafficking in a new and novel way. Findings: Our approach identified new innovative datasets that have previously been overlooked or, at best, undervalued. For example, the JAM OS-AI approach identified critical 'dark agent' lynchpins within human trafficking that are difficult to detect and harder to connect to actors and agents within a network. Our preliminary data suggests this is in part due to the fact that ‘dark agents’ in extant research have been difficult to detect and potentially much harder to directly connect to the actors and organizations in human trafficking networks. Our research demonstrates that using new investigative techniques such as OS-AI-aided JAM introduces a powerful toolset to increase understanding of human trafficking and transnational crime and illuminate networks that, to date, avoid global law enforcement scrutiny.

Keywords: human trafficking, open-source intelligence, transnational crime, human security, international human rights, intelligence analysis, JAM OS-AI, Dark Money

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Authors: Brian Smith

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This paper seeks to contrast the contemporary anti-immigration rhetoric since the US pullout of Afghanistan with the debate about enemy aliens in the early modern period. In the seventeenth century, Sir Edward Coke declared that “infidels” should be seen as perpetui inimici (perpetual enemies) since their values were inimical to those of Christian states. As such, they could be perpetually excluded and denied legal standing. Even at that time, these anti-“infidel” arguments clashed with the natural law tradition of hospitality, which assumed that states had a moral responsibility to admit and care for strangers. In particular, this paper looks at the conflict between Hugo Grotius, Samuel Pufendorf, and John Locke. Grotius argues that states have a duty to admit foreigners. He goes out of his way to plan for the admittance of Jewish immigrants. In contrast, Pufendorf claimed that sovereigns had a duty exclude foreigners who would alter the constitutional character of the state. Much like Grotius, Locke argued that non-Christian peoples should be treated as friends and admitted without reservation.

Keywords: enemy aliens, perpetual enemies, hospitality, refugees

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Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

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Authors: Montri Meenium

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The purpose of this research was to examine youths’ analysis and evaluation of three protagonists, one female and two males involved in sexual relationship in the stage play “Kaki” held by the Faculty of Liberal Arts, Prince of Songkla University. The interviews were conducted with 10 youths in the production team and 10 audience youths, totalling 20. The findings, which were presented in the form of a descriptive analysis, showed that all the 10 youths in the production team and the 10 audience youths did not accept the behaviour of the protagonists: the female who committed adultery and the males who were corrupted by power, had sexual relationship with a married woman and deceived people. The youths, however, knew that such behaviour resulted from being overpowered by human passion, especially infatuation, which was in accordance with the theme of the play. It was suggested that the story twines ideology or points of view that defy moral and ethics, prompting questions to be asked. Hence, the stage play can be used as an instrument to develop critical thinking in youths.

Keywords: descriptive analysis, protagonists, youths, stage-play

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Authors: Abdelrhman Elagez, Rolla Monib

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This empirical study investigates the observed risks associated with adopting Artificial Intelligence (AI) technologies in the construction industry and proposes potential mitigation strategies. While AI has transformed several industries, the construction industry is slowly adopting advanced technologies like AI, introducing new risks that lack critical analysis in the current literature. A comprehensive literature review identified a research gap, highlighting the lack of critical analysis of risks and the need for a framework to measure and mitigate the risks of AI implementation in the construction industry. Consequently, an online survey was conducted with 24 project managers and construction professionals, possessing experience ranging from 1 to 30 years (with an average of 6.38 years), to gather industry perspectives and concerns relating to AI integration. The survey results yielded several significant findings. Firstly, respondents exhibited a moderate level of familiarity (66.67%) with AI technologies, while the industry's readiness for AI deployment and current usage rates remained low at 2.72 out of 5. Secondly, the top-ranked barriers to AI adoption were identified as lack of awareness, insufficient knowledge and skills, data quality concerns, high implementation costs, absence of prior case studies, and the uncertainty of outcomes. Thirdly, the most significant risks associated with AI use in construction were perceived to be a lack of human control (decision-making), accountability, algorithm bias, data security/privacy, and lack of legislation and regulations. Additionally, the participants acknowledged the value of factors such as education, training, organizational support, and communication in facilitating AI integration within the industry. These findings emphasize the necessity for tailored risk assessment frameworks, guidelines, and governance principles to address the identified risks and promote the responsible adoption of AI technologies in the construction sector.

Keywords: risk management, construction, artificial intelligence, technology

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Authors: Joshna Rani S., Ahmadi Banu

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Artificial Intelligence (AI) has a significant task to carry out in the medical care contributions of things to come. As AI, it is the essential capacity behind the advancement of accuracy medication, generally consented to be a painfully required development in care. Albeit early endeavors at giving analysis and treatment proposals have demonstrated testing, we anticipate that AI will at last dominate that area too. Given the quick propels in AI for imaging examination, it appears to be likely that most radiology, what's more, pathology pictures will be inspected eventually by a machine. Discourse and text acknowledgment are now utilized for assignments like patient correspondence and catch of clinical notes, and their utilization will increment. The best test to AI in these medical services areas isn't regardless of whether the innovations will be sufficiently skilled to be valuable, but instead guaranteeing their appropriation in day by day clinical practice. For far reaching selection to happen, AI frameworks should be affirmed by controllers, coordinated with EHR frameworks, normalized to an adequate degree that comparative items work likewise, instructed to clinicians, paid for by open or private payer associations, and refreshed over the long haul in the field. These difficulties will, at last, be survived, yet they will take any longer to do as such than it will take for the actual innovations to develop. Therefore, we hope to see restricted utilization of AI in clinical practice inside 5 years and more broad use inside 10 years. It likewise appears to be progressively evident that AI frameworks won't supplant human clinicians for a huge scope, yet rather will increase their endeavors to really focus on patients. Over the long haul, human clinicians may advance toward errands and work plans that draw on remarkably human abilities like sympathy, influence, and higher perspective mix. Maybe the lone medical services suppliers who will chance their professions over the long run might be the individuals who will not work close by AI

Keywords: artificial intellogence, health care, breast cancer, AI applications

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Authors: Hoora Beheshti Haradasht, Abooali Golzary

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Environmental education plays a pivotal role in shaping the future stewards of our planet. Leveraging the power of artificial intelligence (AI) in this endeavor presents an innovative approach to captivate and educate elementary school children about environmental sustainability. This paper explores the application of AI technologies in designing interactive and personalized learning experiences that foster curiosity, critical thinking, and a deep connection to nature. By harnessing AI-driven tools, virtual simulations, and personalized content delivery, educators can create engaging platforms that empower children to comprehend complex environmental concepts while nurturing a lifelong commitment to protecting the Earth. With the pressing challenges of climate change and biodiversity loss, cultivating an environmentally conscious generation is imperative. Integrating AI in environmental education revolutionizes traditional teaching methods by tailoring content, adapting to individual learning styles, and immersing students in interactive scenarios. This paper delves into the potential of AI technologies to enhance engagement, comprehension, and pro-environmental behaviors among elementary school children. Modern AI technologies, including natural language processing, machine learning, and virtual reality, offer unique tools to craft immersive learning experiences. Adaptive platforms can analyze individual learning patterns and preferences, enabling real-time adjustments in content delivery. Virtual simulations, powered by AI, transport students into dynamic ecosystems, fostering experiential learning that goes beyond textbooks. AI-driven educational platforms provide tailored content, ensuring that environmental lessons resonate with each child's interests and cognitive level. By recognizing patterns in students' interactions, AI algorithms curate customized learning pathways, enhancing comprehension and knowledge retention. Utilizing AI, educators can develop virtual field trips and interactive nature explorations. Children can navigate virtual ecosystems, analyze real-time data, and make informed decisions, cultivating an understanding of the delicate balance between human actions and the environment. While AI offers promising educational opportunities, ethical concerns must be addressed. Safeguarding children's data privacy, ensuring content accuracy, and avoiding biases in AI algorithms are paramount to building a trustworthy learning environment. By merging AI with environmental education, educators can empower children not only with knowledge but also with the tools to become advocates for sustainable practices. As children engage in AI-enhanced learning, they develop a sense of agency and responsibility to address environmental challenges. The application of artificial intelligence in elementary environmental education presents a groundbreaking avenue to cultivate environmentally conscious citizens. By embracing AI-driven tools, educators can create transformative learning experiences that empower children to grasp intricate ecological concepts, forge an intimate connection with nature, and develop a strong commitment to safeguarding our planet for generations to come.

Keywords: artificial intelligence, environmental education, elementary children, personalized learning, sustainability

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Authors: Zahra Mohagheghian, Fatema Agharebparast

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Professional ethic is a subject that has been an issue today, so most of the businesses, including the teaching profession, understand the need and importance of it. So they need to develop a code of professional ethics for their own. In this regard, this study seeks to answer the question, with respect to the integrity of the Qur'an (Nahl / 89), is it possible to contemplate the divine teachers conduct to extract the divine pattern for teaching and training? In the code of conduct for divine teachers what are the most important moral obligations and duties of the teaching professionals? The results of this study show that the teaching of Khidr, according to the Quran’s verses, Abundant and subtle hints emphasized that it can be as comprehensive and divine pattern used in teaching and in the drafting of the charter of professional ethics of teachers used it. Also, the results show that in there have been many ethical principles in prophet Khidr’s teaching pattern.The most important ethical principles include: Student assessment, using objective and not subjective examples, assessment during teaching, flexibility, and others. According to each of these principles can help teachers achieve their educational goals and lead human being in their path toward spiritual evaluation.

Keywords: professional ethics, teaching-learning process, teacher, student, Quran

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Authors: Siti Aisyah

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This article discusses character education which is a very basic education for early childhood with the aim of instilling moral values to prevent unacceptable behaviours. Children can absorb good character when they are in a supportive environment, for that schools should understand and implement character education in the learning process. In the school environment, good character education and habituation can be developed. All parties in the school should be involved, especially the teachers. This research discusses how teachers apply characters on the values of responsibility, honesty, discipline, love and compassion, caring, courage, independence, hard work, mutual cooperation, courtesy, justice, self-control and tolerance. The respondents of this study were teachers involving 200 children from all over Indonesia. The methodology used was a survey method with the result that more than 80% of teachers have been able to exhibit the expected behaviours. The survey was conducted based on observations, types of tasks and assessed performance. The character values can be optimally taught in the school environment based on the teacher's ability to implement them. Through the character education in schools, children can also instil a positive outlook on life.

Keywords: teachers, character strengthening, early childhood, behavior

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Authors: Tracy B. E. Omorogiuwa

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Human trafficking also known as modern-day slavery can be seen as an effort by some privileged and criminally minded persons to take advantage of vulnerable individuals for their economic gains. Some factors; poverty, unemployment, poor educational opportunities, ignorance and traditional attitudes are attributed as causes and psychological, sexual, moral and health problems as impacts of human trafficking. This study examines the phenomenon of human trafficking in Benin City, one of the cities in Nigeria, situated as a source of trafficked persons for exploitation in Europe and African countries. Even though the Nigerian government and Non-governmental organizations have made considerable efforts in the past to reduce the incidence of human trafficking, the result has been an adjustment in the personality of the trafficked persons rather than professional measures to combat the issue. Hence, the study adopts the focused group discussions as a method for data collection; to sort the opinions of community members towards the understanding of the phenomenon. In addition, this paper provides social work implications to address the issue of human trafficking in the Benin City, Nigeria.

Keywords: human trafficking, trafficking in persons, modern-day slavery, social work implication

Procedia PDF Downloads 179