Abstract: Artificial Intelligence (AI) is revolutionizing organizational dynamics by reshaping both structures and processes. This paper explores how AI-driven innovations are transforming organizational frameworks, from hierarchical adjustments to decentralized decision-making models. It examines the impact of AI on various processes, including workflow automation, data analysis, and enhanced decision support systems. Through case studies and empirical research, the paper highlights the benefits of AI in improving efficiency, driving innovation, and fostering agility within organizations. Additionally, it addresses the challenges associated with (...) AI integration, such as resistance to change, ethical concerns, and data security issues. The paper concludes by offering insights into future trends and implications for organizations navigating the evolving landscape of AI-driven change. (shrink)

This argument explores the transformative potential of AI-driven legislative simulations for creating inclusive, equitable, and globally adaptable laws. By using predictive modeling and adaptive frameworks, these simulations can account for diverse cultural, social, and economic contexts. The argument emphasizes the need for universal ethical safeguards, trust-building measures, and phased implementation strategies. Case studies of successful applications in governance and conflict resolution demonstrate the feasibility and efficacy of this approach. The conclusion highlights AI’s role in democratizing governance and ensuring laws (...) are more equitable and culturally sensitive. (shrink)

Abstract: Artificial Intelligence (AI) is transforming the agricultural sector, enhancing both productivity and sustainability. This paper delves into the impact of AI technologies on agriculture, emphasizing their application in precision farming, predictive analytics, and automation. AI-driven tools facilitate more efficient crop and resource management, leading to higher yields and a reduced environmental footprint. The paper explores key AI technologies, such as machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource use. (...) Additionally, it discusses challenges like data privacy, barriers to technology adoption, and the ethical implications of AI in farming. Integrating AI into agricultural practices holds the promise of greater efficiency and sustainability, paving the way for future innovations. (shrink)

We investigate the application of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model for forecasting air pollution levels based on historical data. Our experimental setup uses real-world air quality datasets from multiple regions, containing measurements of pollutants like PM2.5, PM10, CO, NO2, and SO2, alongside meteorological data such as temperature, humidity, and wind speed. The models are trained, validated, and tested using a split dataset, and their accuracy is evaluated using performance metrics like Mean (...) Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared. (shrink)

Over the last few years, personal health monitoring wearable devices have emerged as innovative applications of Artificial Intelligence (AI) in the healthcare industry as they help in real time analysis and prediction of health standardized check-ups and health management. To navigate through the current trends, new technologies and developments, the prospects are as follows: The article also gives a logical look at the state of the art of such devices, enumerating the advantages and drawbacks, as well as outlining the main (...) ethical issues. In terms of method, it also discusses different AI approaches, data acquisition procedures, and those integrating systems used in the context of health monitoring. The finding and implication section discusses the findings of the current trend and the possibility of improvement in application and user acceptance. The last section provides a prognosis on what the area of personal health monitoring might look like in the future, as well as the position of AI in the optimisation of patient experience. (shrink)

This research explores the transformative role of artificial intelligence (AI) in strategic decision-making and business development, highlighting its capacity to enhance strategy execution, optimize operations, and foster innovation through advanced methodologies such as machine learning, predictive analytics, and natural language processing. By employing a mixed-methods approach that combines deductive and inductive research designs, crosssectional case analysis, and a review of empirical literature, the study underscores AI’s critical role in delivering datadriven insights, accurate forecasting, and robust simulations, positioning it as a (...) cornerstone of competitive advantage in dynamic global markets. However, key challenges such as ethical concerns, data quality issues, and disparities in organizational AI readiness are identified, emphasizing the need for robust governance frameworks and interdisciplinary collaboration to ensure responsible adoption. The findings provide actionable guidance for businesses, policymakers, and researchers, advocating for the alignment of AI implementation with strategic goals and ethical standards to harness its full potential for sustainable growth and innovation. (shrink)

Urbanization poses significant challenges to healthcare systems, including overcrowded hospitals, inequitable access to care, and rising costs. Artificial Intelligence (AI) and the Internet of Things (IoT) offer transformative solutions for optimizing healthcare delivery in smart cities. This paper explores how AI-driven predictive analytics, combined with IoT-enabled wearable devices and telemedicine platforms, can enhance patient outcomes, streamline resource allocation, and reduce urban health disparities. By analyzing real-time health data and predicting disease outbreaks, this study demonstrates the potential of AI to (...) revolutionize urban healthcare systems. Experimental results highlight improvements in diagnostic accuracy, emergency response times, and operational efficiency, paving the way for equitable and sustainable healthcare in smart cities. (shrink)

Abstract: The incorporation of Artificial Intelligence (AI) into educational technology has dramatically transformed learning through Intelligent Tutoring Systems (ITS). These systems utilize AI to offer personalized, adaptive instruction tailored to each student's needs, thereby improving learning outcomes and engagement. This paper examines the development and impact of ITS, focusing on AI technologies such as machine learning, natural language processing, and adaptive algorithms that drive their functionality. Through various case studies and applications, it illustrates how ITS have revolutionized traditional educational methods (...) and addresses the challenges and limitations, including data privacy and system biases. The discussion also covers future directions in ITS development, highlighting emerging trends and potential advancements. The goal is to provide a thorough understanding of how ITS use AI to enhance educational experiences and suggest avenues for future research in this evolving field. (shrink)

Abstract: As the frequency and sophistication of cyberattacks continue to rise, organizations face increasing challenges in safeguarding their digital infrastructures. Traditional cybersecurity measures often struggle to keep pace with rapidly evolving threats, creating a pressing need for more adaptive and proactive solutions. Artificial Intelligence (AI) has emerged as a transformative force in this domain, offering enhanced capabilities for detecting, analyzing, and preventing cyberattacks in real- time. This paper explores the pivotal role of AI in strengthening cybersecurity defenses by leveraging machine (...) learning algorithms, predictive analytics, and automation to anticipate and mitigate potential threats before they manifest. Furthermore, it examines AI's ability to evolve with emerging attack vectors, providing a dynamic response to an ever-changing threat landscape. The paper also addresses the limitations and ethical considerations surrounding AI-driven cybersecurity, advocating for a balanced approach to its deployment. Through this exploration, the research underscores how AI is redefining the future of cyber defense by shifting the focus from reactive to proactive strategies. (shrink)

The rapid evolution of the financial sector, particularly in banking and fintech, necessitates continuous innovation in financial product development and testing. However, challenges such as data privacy, regulatory compliance, and the limited availability of diverse datasets often hinder the effective development and deployment of new products. This research investigates the transformative potential of AI-driven synthetic data generation as a solution for accelerating innovation in financial product development. Synthetic data, generated through advanced AI techniques such as Generative Adversarial Networks (GANs), (...) Variational Autoencoders (VAEs), and Transformer-based models, can simulate real-world financial scenarios with a high degree of fidelity while preserving privacy and compliance standards. The use of synthetic data enables financial institutions and fintech companies to conduct rigorous testing, modeling, and validation of new products and services without relying on sensitive customer data. By generating realistic yet artificial datasets, organizations can explore a broader range of scenarios, including rare or extreme market conditions, thus enhancing the robustness and reliability of their financial models. This paper provides a comprehensive analysis of the underlying methodologies for synthetic data generation, focusing on their application to financial product development. It delves into the specific architectures and frameworks used in generating synthetic data, including GANs, VAEs, and synthetic minority over-sampling techniques (SMOTE), and examines their respective advantages and limitations. The paper also addresses the critical issue of ensuring the quality and utility of synthetic data, emphasizing metrics such as statistical similarity, privacy preservation, and applicability to real-world use cases. The discussion extends to the ethical and regulatory implications of deploying AI-driven synthetic data in finance, highlighting the need for transparent and explainable AI models to ensure trust and compliance. Moreover, the research explores practical case studies where financial institutions and fintech firms have successfully implemented synthetic data to develop and test new products, demonstrating significant reductions in time-to-market and development costs. One of the key contributions of this research is the exploration of how AI-driven synthetic data generation can facilitate the development of innovative financial products such as algorithmic trading strategies, risk management tools, credit scoring models, and fraud detection systems. By simulating diverse market behaviors and customer interactions, synthetic data enables the fine-tuning of algorithms and models to achieve higher accuracy and performance. Additionally, the paper discusses the integration of synthetic data generation into existing financial data ecosystems, proposing a framework for leveraging hybrid datasets that combine synthetic and real data to optimize model training and validation. The potential for synthetic data to drive collaborative innovation in finance is also considered, as it allows multiple stakeholders, including banks, fintech startups, and regulators, to share and analyze data without compromising confidentiality or privacy. The research also addresses the limitations and challenges associated with synthetic data generation in the financial domain, including issues related to data representativeness, overfitting, and the potential misuse of synthetic datasets. It emphasizes the need for ongoing research to develop more sophisticated algorithms that can generate highly realistic and diverse financial data. Furthermore, it identifies areas for future exploration, such as the use of federated learning and differential privacy techniques to enhance the security and privacy of synthetic data generation processes. The findings of this paper underscore the importance of AI-driven synthetic data generation as a catalyst for innovation in banking and fintech, providing a secure, scalable, and cost-effective means to develop, test, and validate new financial products and services. As the financial industry continues to evolve, the role of synthetic data in shaping the future of financial product development will become increasingly critical, paving the way for more efficient and innovative financial solutions. (shrink)

In today’s rapidly evolving business landscape, organizations must continuously adapt to stay competitive. AI-driven human resource (HR) analytics has emerged as a strategic tool to enhance workforce agility and inform decision-making processes. By leveraging advanced algorithms, machine learning models, and predictive analytics, HR departments can transform vast data sets into actionable insights, driving talent management, employee engagement, and overall organizational efficiency. AI’s ability to analyze patterns, forecast trends, and offer data-driven recommendations empowers HR professionals to make proactive decisions (...) in hiring, skill development, performance management, and retention. (shrink)

Water scarcity and inefficient water management are critical challenges for rapidly growing urban areas. Traditional water distribution systems often suffer from leaks, wastage, and inequitable access, exacerbating resource shortages. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban water management by enabling real-time monitoring, predictive maintenance, and efficient resource allocation. By integrating data from smart meters, pressure sensors, and weather forecasts, cities can reduce water losses, improve distribution efficiency, and ensure equitable access. Experimental results demonstrate significant (...) improvements in leak detection, water conservation, and infrastructure reliability, offering a sustainable blueprint for urban water management in smart cities. (shrink)

The growing volume of urban waste poses significant environmental and economic challenges for cities worldwide. Traditional waste management systems often rely on inefficient collection routes, inadequate recycling processes, and excessive landfill usage. This paper explores how Artificial Intelligence (AI) and IoT technologies can revolutionize waste management in smart cities by enabling real-time monitoring, automated sorting, and optimized collection routes. By integrating data from smart bins, robotic sorting systems, and predictive analytics, cities can achieve zero-waste goals and promote circular economy practices. (...) Experimental results demonstrate significant improvements in waste segregation accuracy, collection efficiency, and recycling rates, offering a sustainable blueprint for urban waste management. (shrink)

Urban parking systems are a significant contributor to traffic congestion and driver frustration, with studies showing that up to 30% of urban traffic is caused by drivers searching for parking. Traditional parking systems often lack real-time data and adaptability, leading to inefficiencies such as overfilled lots and underutilized spaces. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban parking by enabling real-time parking space detection, demand forecasting, and dynamic pricing. By integrating data from IoT sensors, traffic (...) systems, and mobile applications, cities can reduce congestion, improve parking availability, and enhance the overall urban mobility experience. Experimental results demonstrate significant improvements in parking efficiency, traffic flow, and user satisfaction, offering a sustainable blueprint for smart urban parking systems. (shrink)

Buildings account for a significant portion of global energy consumption and carbon emissions, making energy efficiency a critical focus for urban sustainability. Traditional building management systems often lack the adaptability and precision needed to optimize energy usage dynamically. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance energy efficiency in smart buildings by enabling real-time monitoring, predictive maintenance, and adaptive control systems. By integrating data from smart meters, occupancy sensors, and environmental monitors, cities can reduce energy waste, (...) lower carbon footprints, and improve occupant comfort. Experimental results demonstrate significant improvements in energy savings, operational costs, and environmental impact, offering a sustainable blueprint for smart building management. (shrink)

Urban wastewater management is a critical component of sustainable water cycles, but traditional systems often struggle with inefficiencies such as high operational costs, resource wastage, and environmental pollution. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban wastewater management by enabling real-time monitoring, predictive maintenance, and resource recovery. By integrating data from IoT sensors, water quality monitors, and treatment plants, cities can improve water quality, reduce operational costs, and recover valuable resources such as energy and nutrients. (...) Experimental results demonstrate significant improvements in treatment efficiency, resource recovery rates, and environmental impact, offering a sustainable blueprint for smart urban wastewater systems. (shrink)

Urban lighting systems are essential for safety, security, and quality of life, but they often consume significant energy and lack adaptability to changing conditions. Traditional lighting systems rely on fixed schedules and manual adjustments, leading to inefficiencies such as over-illumination and energy waste. This paper explores how Artificial Intelligence (AI) and IoT technologies can optimize urban lighting by enabling real-time adjustments, energy savings, and adaptive illumination based on environmental conditions and human activity. By integrating data from motion sensors, weather forecasts, (...) and traffic systems, cities can reduce energy consumption, enhance safety, and improve the quality of life for residents. Experimental results demonstrate significant improvements in energy efficiency, lighting quality, and operational costs, offering a sustainable blueprint for smart urban lighting systems. (shrink)

Air pollution is a critical challenge for urban areas, contributing to public health crises and environmental degradation. Traditional air quality monitoring systems often lack the granularity and adaptability needed to address dynamic pollution sources and patterns. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance air quality management in smart cities by enabling real-time monitoring, pollution source identification, and adaptive mitigation strategies. By integrating data from IoT sensors, satellite imagery, and traffic systems, cities can reduce pollution levels, (...) improve public health outcomes, and promote sustainable urban environments. Experimental results demonstrate significant improvements in pollution detection accuracy, mitigation efficiency, and public awareness, offering a sustainable blueprint for urban air quality management. (shrink)

Noise pollution is a growing concern in urban areas, contributing to public health issues such as stress, sleep disturbances, and hearing loss. Traditional noise monitoring systems often lack the granularity and adaptability needed to address dynamic noise sources and patterns. This paper explores how Artificial Intelligence (AI) and IoT technologies can enhance noise pollution management in smart cities by enabling real-time monitoring, source identification, and adaptive mitigation strategies. By integrating data from IoT sensors, traffic systems, and urban infrastructure, cities can (...) reduce noise levels, improve public health outcomes, and enhance the quality of life for residents. Experimental results demonstrate significant improvements in noise detection accuracy, mitigation efficiency, and public awareness, offering a sustainable blueprint for urban noise pollution management. (shrink)

The increasing complexity and demands of next-generation networks necessitate the integration of Artificial Intelligence (AI) to enhance their efficiency and security. This article explores advancements in AI-driven communication systems, focusing on optimizing network performance, ensuring robust security measures, and addressing the challenges of scalability and real-time adaptability. By analyzing case studies, emerging technologies, and recent research, this study highlights AI's transformative potential in redefining communication systems for future applications.

This paper presents the development of an AI-driven emotional support chatbot designed to provide personalized and real-time mental health care. The chatbot leverages natural language processing and machine learning algorithms to respond to users' emotional needs, offering support in multiple languages to improve accessibility. The chatbot also integrates professional mental health resources to enhance its effectiveness. This study aims to bridge the gap between traditional mental health support systems and the growing demand for digital mental health tools. Key results (...) demonstrate increased user engagement, personalized care, and improved mental health outcomes. This approach highlights the chatbot’s potential to revolutionize mental health support systems. (shrink)

Urban resilience is critical for ensuring the sustainability and adaptability of cities in the face of growing challenges such as climate change, population growth, and infrastructure degradation. Predictive analytics, powered by Artificial Intelligence (AI) and the Internet of Things (IoT), offers a transformative approach to enhancing urban resilience. This paper explores how AI-driven predictive analytics can optimize disaster preparedness, infrastructure maintenance, and resource allocation in smart cities. By integrating real-time data from IoT sensors with advanced machine learning models, cities (...) can proactively address vulnerabilities and improve decision-making. This study highlights key applications, challenges, and future directions for leveraging predictive analytics to build resilient urban ecosystems. (shrink)

Ikigai is a Japanese concept, which, in brief, refers to the “reason or purpose to live”. I-risks have been identified as a category of risks complementing x- risks, i.e., existential risks, and s-risks, i.e., suffering risks, which describes undesirable future scenarios in which humans are deprived of the pursuit of their individual ikigai. While some developments in AI increase i-risks, there are also AI-driven virtual opportunities, which reduce i-risks by increasing the space of potential ikigais, largely due to developments (...) in generative AI, virtual worlds as well as AI-driven hyper-personalization. The purpose of this paper is to present a first attempt to map the potential AI-driven virtual hyper-personalised ikigai universe. Moreover, challenges and further ideas are presented. (shrink)

Traditional medical practices and relationships are changing given the widespread adoption of AI-driven technologies across the various domains of health and healthcare. In many cases, these new technologies are not specific to the field of healthcare. Still, they are existent, ubiquitous, and commercially available systems upskilled to integrate these novel care practices. Given the widespread adoption, coupled with the dramatic changes in practices, new ethical and social issues emerge due to how these systems nudge users into making decisions and (...) changing behaviours. This article discusses how these AI-driven systems pose particular ethical challenges with regards to nudging. To confront these issues, the value sensitive design approach is adopted as a principled methodology that designers can adopt to design these systems to avoid harming and contribute to the social good. The AI for Social Good factors are adopted as the norms constraining maleficence. In contrast, higher-order values specific to AI, such as those from the EU High-Level Expert Group on AI and the United Nations Sustainable Development Goals, are adopted as the values to be promoted as much as possible in design. The use case of Amazon Alexa's Healthcare Skills is used to illustrate this design approach. It provides an exemplar of how designers and engineers can begin to orientate their design programs of these technologies towards the social good. (shrink)

The exponential growth of data storage requirements has become a pressing challenge in hybrid cloud environments, necessitating efficient data deduplication methods. This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework (...) achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

Emotion detection and management have emerged as pivotal areas in humancomputer interaction, offering potential applications in healthcare, entertainment, and customer service. This study explores the use of deep learning (DL) models to enhance emotion recognition accuracy and enable effective emotion regulation mechanisms. By leveraging large datasets of facial expressions, voice tones, and physiological signals, we train deep neural networks to recognize a wide array of emotions with high precision. The proposed system integrates emotion recognition with adaptive management strategies that provide (...) personalized feedback and interventions based on detected emotional states. Our approach surpasses traditional machine learning methods, demonstrating superior performance in real-time applications. We also explore the ethical implications and challenges associated with deploying such systems, particularly regarding privacy concerns and the potential for misuse. Through extensive experiments, our model achieved an average accuracy rate of 92%, highlighting its robustness across different environments and user demographics. This research not only contributes to the growing field of affective computing but also lays the groundwork for future developments in emotionally intelligent systems. (shrink)

The Grievance Management System is a web-based platform designed to enhance the efficiency and transparency of handling public grievances. By introducing role-based access for users, moderators, and government officials, the system ensures that grievances are systematically reviewed, prioritized, and resolved. Users can submit grievances, track their status, and receive notifications regarding updates. Moderators are tasked with verifying the validity of each grievance and assigning it a priority level before passing it on to government officials for action. Government officials, in turn, (...) have the ability to filter grievances by priority and submission date, helping them manage and resolve issues more effectively. The system integrates technologies such as Flask for backend logic, MySQL for database management, and natural language processing (NLP) to classify grievances into relevant departments. It also prevents rejected grievances from being re- submitted and delivers real-time notifications to users, ensuring an efficient and transparent grievance handling process. This project aims to improve the accountability and responsiveness of government departments in addressing public complaints. (shrink)

The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable calorie estimation across different food items. This tool has the potential to revolutionize personal health management by promoting healthier eating habits.

In the rapidly evolving digital landscape, cyber threats are becoming increasingly sophisticated, making traditional security measures inadequate. Advanced Threat Detection (ATD) leveraging Artificial Intelligence (AI)-driven anomaly detection systems offers a proactive approach to identifying and mitigating cyber threats in real time. This paper explores the integration of AI, particularly machine learning (ML) and deep learning (DL) techniques, in anomaly detection to enhance cybersecurity defenses. By analyzing vast amounts of network traffic, user behavior, and system logs, AI-driven models can (...) identify deviations from normal patterns, enabling early threat detection and prevention. These systems excel in detecting zero-day attacks, insider threats, and advanced persistent threats (APTs), which often bypass conventional rule-based security mechanisms. Additionally, we discuss the challenges of AI-based anomaly detection, including false positives, model interpretability, and adversarial attacks. The findings emphasize the need for continuous learning and adaptive security frameworks to ensure robust cyber threat detection. The study concludes that AIdriven anomaly detection significantly enhances threat intelligence and response capabilities, making it a vital component of modern cybersecurity strategies. (shrink)

The study was based on the topic of the way AI-driven Chatbots influence customer loyalty and satisfaction in service industries. The overall research provided brief background information about the topic, along with research aim, objectives and questions. Furthermore, the problem statement and the rationale of the research were stated in the next part. The literature review part of the topic was focused on elaborating several factors that help AIpowered Chatbots in customer satisfaction and loyalty. The methodology part also mentioned (...) several methods including this research. The findings and discussion provided a brief analysis of the overall topic. Lastly, the study mentioned conclusions and recommendations based on the topic. (shrink)

The use of Artificial Intelligence (AI) in educational gamification marks a significant advancement, transforming traditional learning methods by offering interactive, adaptive, and personalized content. This approach makes historical content more relatable and promotes active learning and exploration. This research presents an innovative approach to heritage education, combining AI and gamification, explicitly targeting the Silk Roads. It represents a significant progression in a series of research, transitioning from basic 2D textual interactions to a 3D environment using photogrammetry, combining historical authenticity and (...) immersive gameplay. It features AI-driven characters developed in Unreal Engine, which provides heritage insights augmented by personalized interactions, highlighting the importance of collaborative tools like interactive quizzes for enriching education and promoting critical thinking and community among learners. In conclusion, this research underlines the transformative impact of AI and gamification in heritage education enhancement, integrating real-time voice conversation and interactive pedagogical methods into immersive 3D environments as a supplementary tool. (shrink)

Concerns about threats to human autonomy feature prominently in the field of AI ethics. One aspect of this concern relates to the use of AI systems for problematically manipulative influence. In response to this, the European Union’s draft AI Act (AIA) includes a prohibition on AI systems deploying subliminal techniques that alter people’s behavior in ways that are reasonably likely to cause harm (Article 5(1)(a)). Critics have argued that the term ‘subliminal techniques’ is too narrow to capture the target cases (...) of AI-based manipulation. We propose a definition of ‘subliminal techniques’ that (a) is grounded on a plausible interpretation of the legal text; (b) addresses all or most of the underlying ethical concerns motivating the prohibition; (c) is defensible from a scientific and philosophical perspective; and (d) does not over-reach in ways that impose excessive administrative and regulatory burdens. The definition is meant to provide guidance for design teams seeking to pursue responsible and ethically aligned AI innovation. (shrink)

Throughout history, humans have sought spiritual experiences to find meaning, solace, or answers to life’s complexities. In religious settings, particularly in charismatic gatherings, phenomena such as people falling to the ground, rolling over, or convulsing after being touched or waved at by religious figures have been reported across many traditions. While believers may interpret these occurrences as signs of divine intervention, the underlying reasons behind these phenomena could be deeply rooted in psychological, neurological, and social factors.

By embracing AI-driven HR analytics, organizations can anticipate market shifts, prepare their workforce for future challenges, and stay ahead of the competition. This study outlines the essential components of AI-driven HR analytics, demonstrates its impact on workforce agility, and concludes with potential future enhancements to further optimize HR functions. Key words: Predictive Workforce Analytics, Talent Optimization, Machine Learning in.

Indian Sign Language (ISL) users face challenges communicating effectively with English speakers, primarily due to a lack of accessible, real-time translation tools. Existing systems often struggle with context and natural flow, leading to misunderstandings and reduced usability. This project proposes an AI-powered system designed to overcome these limitations, incorporating speech recognition and natural language processing (NLP) to facilitate seamless interaction between ISL and English. The tool aims to bridge the language gap, providing ISL users with an accessible and efficient solution (...) that promotes inclusivity and autonomy. . (shrink)

Birds are an integral part of our ecosystem, playing diverse roles in pollination, seed dispersal, pest control, and ecological balance. Monitoring bird populations and identifying species are crucial for understanding biodiversity, assessing ecosystem health, and implementing conservation strategies. Traditionally, bird species identification has relied on manual observation, which requires significant expertise and time. However, this process is often prone to human error and inefficiency, especially when distinguishing between visually similar species. As global biodiversity faces increasing threats, there is a pressing (...) need for innovative and automated solutions that can accurately identify bird species. (shrink)

As the integration of Artificial Intelligence (AI) and cloud computing transforms the insurance industry, it is undergoing a major breakthrough. With these technologies, insurers can modernize operations, improve the customer experience and make better decisions using real time data and predictive analytics. This paper aims to explore why AI and cloud play such critical roles in shifting insurance practice from legacy systems modernization, to data governance and regulatory compliance to workforce readiness. Today, world-class AI powered tools and cloud platforms such (...) as AWS and Snowflake provide hyper personalization, one-touch automated claims, fraud detection, etc. IoT data can be used to perform dynamic risk assessment for insurers and even to offer products that start with low code and no code solutions to make technology available across an organization. Edge computing, as well as sustainability across insurance products, shows that there is more innovation potential. Yet, these technologies cannot be fully integrated. However, the industry must figure out how to navigate evolving regulations, employ explainability in AI-driven decisions, and address vendor dependency issues. Governance frameworks need to be strengthened;the workforce needs to be upskilled, collaborate with InsurTech startups, and so on. The paper ends with a forward-looking view, offering potential expansion into underserved markets, more advanced predictive analytics, and better customer contact through omnichannel support via AI enabled risk coaching. Embracing these advances, insurers can chart a new course and lead the digital transformation era by providing more value to stakeholders in an evolving environment. (shrink)

This paper explores the application of AI-driven HR analytics in shaping workforce agility, focusing on how real-time data collection, analysis, and modeling foster an adaptable workforce. It highlights the role of predictive analytics in forecasting workforce needs, identifying skill gaps, and optimizing talent deployment. Additionally, the paper discusses how AI enhances strategic decision-making by providing precise metrics and insights into employee behavior, productivity, and satisfaction. The integration of AI into HR systems ultimately shifts HR from a traditionally reactive to (...) a highly proactive, data-driven function. (shrink)

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial (...) intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy. (shrink)

Artificial Intelligence (AI) is rapidly transforming Human Resource Management (HRM) by enhancing the efficiency and effectiveness of key functions such as recruitment, performance management, and employee engagement. This paper explores the integration of AI technologies in HRM, focusing on their potential to revolutionize these critical areas. In recruitment, AI-driven tools streamline candidate sourcing, screening, and selection processes, leading to more accurate and unbiased hiring decisions. Performance management is similarly transformed, with AI enabling continuous, data-driven feedback and personalized development (...) plans that align with organizational goals. Furthermore, AI facilitates more engaging and tailored employee experiences by leveraging predictive analytics to anticipate and address employee needs. Despite these advancements, the paper also addresses the ethical considerations and challenges associated with AI in HRM, including data privacy concerns and the risk of over-reliance on automated systems. Through a comprehensive analysis, this paper aims to provide insights into how AI can be strategically leveraged to optimize HRM practices, while highlighting the need for a balanced approach that integrates human judgment with technological innovation. (shrink)

Today, software instruments support all parts of engineering work, from design to creation. Many engineering processes call for tedious routine appointments and torments with manual handoffs and data storehouses. AI designers train profound brain networks and incorporate them into software structures.

In 2024, the intersection of artificial intelligence (AI) and cybersecurity presents both unprecedented challenges and significant opportunities. This article explores the evolving landscape of AI-driven cyber threats, the advancements in AI-enabled security measures, and the strategic responses required to navigate these new realities. Leveraging statistics, trends, and expert insights, we delve into how organizations can enhance their cybersecurity posture in the face of sophisticated AI threats.

The landscape of cybersecurity is constantly evolving, with adversaries becoming increasingly sophisticated and persistent. This manuscript explores the utilization of artificial intelligence (AI) to address these evolving threats, focusing on the journey from threat detection to autonomous response. By examining AI-driven detection methodologies, advanced threat analytics, and the implementation of autonomous response systems, this paper provides insights into how organizations can leverage AI to strengthen their cybersecurity posture against modern threats. Key words: Ransomware, Anomaly Detection, Advanced Persistent Threats (APTs), (...) Automated Threat Response and Artificial Intelligence. (shrink)

Abstract: In an era of rapid technological advancements, artificial intelligence (AI) has transformed surveillance systems, enhancing security capabilities across the globe. However, the deployment of AI-driven surveillance raises significant ethical concerns, particularly in balancing the need for security with the protection of individual privacy. This paper explores the ethical challenges posed by AI surveillance, focusing on issues such as data privacy, consent, algorithmic bias, and the potential for mass surveillance. Through a critical analysis of the tension between security and (...) privacy, this paper examines the implications of AI technologies on civil liberties and human rights. It also highlights the importance of establishing clear regulatory frameworks and ethical guidelines to ensure that AI surveillance systems operate within boundaries that respect individual freedoms. In navigating this delicate balance, we propose solutions that prioritize transparency, accountability, and the protection of privacy in the digital age. (shrink)

In today’s rapidly evolving financial landscape, the adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies, coupled with the deployment of Business Rules Management Systems (BRMS), has transformed how financial transactions are conducted, monitored, and secured. With fraud, particularly in check deposit transactions, becoming increasingly sophisticated, financial institutions are turning to AI and ML to enhance their risk management strategies. This paper explores the integration of AI-driven models and business rules in financial transactions, focusing on their application in (...) fraud detection and prevention. We also examine the associated challenges, such as data privacy, ethical concerns, and the need for regulatory frameworks, while outlining the opportunities that lie ahead for future financial innovation. (shrink)

In this paper, we propose a methodology to maximize the benefits of interdisciplinary cooperation in AI research groups. Firstly, we build the case for the importance of interdisciplinarity in research groups as the best means to tackle the social implications brought about by AI systems, against the backdrop of the EU Commission proposal for an Artificial Intelligence Act. As we are an interdisciplinary group, we address the multi-faceted implications of the mass-scale diffusion of AI-driven technologies. The result of our (...) exercise lead us to postulate the necessity of a behavioural theory that standardizes the interaction process of interdisciplinary groups. In light of this, we conduct a review of the existing approaches to interdisciplinary research on AI appliances, leading to the development of methodologies like ethics-by-design and value-sensitive design, evaluating their strengths and weaknesses. We then put forth an iterative process theory hinging on a narrative approach consisting of four phases: definition of the hypothesis space, building-up of a common lexicon, scenario-building, interdisciplinary self-assessment. Finally, we identify the most relevant fields of application for such a methodology and discuss possible case studies. (shrink)

Looking into the future is always a risky endeavour, but one way to anticipate the possible future shape of AI-driven societies is to examine the visionary works of some sci-fi writers. Not all sci-fi works have such visionary quality, of course, but some of Stanisław Lem’s works certainly do. We refer here to Lem’s works that explore the frontiers of science and technology and those that describe imaginary societies of robots. We therefore examine Lem’s prose, with a focus on (...) the Cyberiad stories, to see what challenges our future technological societies may face when they entrust their lives to AI technology. For example, what questions should we ask, and what questions do we forget to ask, when developing AI systems and allowing these systems to control our lives. The technologically honed minds of our current technocrats are perhaps too limited to guide us into this future, because AI-based technology is relatively unchartered territory, as any new, complex technology is by nature. Lem’s visions of future societies oriented around AI and robotics portray AI technology in a deeper and more nuanced way than the current technological visions offered by our leading technological prophets. Based on Lem’s visions, what is to come may not turn out to be an AI-driven nirvana. (shrink)

Abstract: Artificial Intelligence (AI) has rapidly advanced, offering significant potential to transform business decision-making. This paper delves into how AI can be harnessed to enhance strategic decision-making within business contexts. It investigates the integration of AI-driven analytics, predictive modeling, and automation, emphasizing their role in improving decision accuracy and operational efficiency. By examining current applications and case studies, the paper underscores the opportunities AI offers, including improved data insights, risk management, and personalized customer experiences. It also addresses the challenges (...) businesses face when adopting AI, such as data privacy concerns, integration difficulties, and the need for skilled professionals. The paper concludes by offering recommendations for successfully implementing AI solutions to maximize benefits while mitigating potential challenges. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into medical imaging represents a transformative shift in healthcare, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. This paper explores the application of AI technologies in the analysis of medical images, focusing on techniques such as convolutional neural networks (CNNs) and deep learning models. We discuss how these technologies are applied to various imaging modalities, including X-rays, MRIs, and CT scans, to enhance disease detection, image segmentation, and diagnostic support. Additionally, the (...) paper addresses the challenges faced in AI-driven medical imaging, including data quality, model interpretability, and ethical considerations. By examining recent advancements and real-world case studies, this paper provides insights into the current state of AI in medical imaging and its potential future directions. The findings highlight the ongoing evolution of AI technologies and their crucial role in advancing medical diagnostics and treatment strategies. (shrink)