THE ROLE OF ARTIFICIAL INTELLIGENCE IN TRANSFORMING TOURISM SERVICES: THE CASE OF DURRËS, ALBANIA

The rapid evolution of Artificial Intelligence (AI) since its inception in the mid-20th century has significantly influenced the field of Natural Language Processing (NLP), transforming it from a rule-based system to a dynamic and adaptive model capable of understanding the complexities of human language. This paper aims to offer a comprehensive review of the various applications and methodologies of AI in NLP, serving as a detailed guide for future research and practical applications. In the early sections, the paper elucidates the indispensable role of AI in NLP, highlighting its transition from symbolic reasoning to a focus on machine learning and deep learning, and its extensive applications in sectors such as healthcare, transportation, and finance. It emphasizes the symbiotic relationship between AI and NLP, facilitated by platforms like AllenNLP, which aid in the development of advanced language understanding models. Further, the paper explores specific AI techniques employed in NLP, including machine learning, Naive Bayes, and Support Vector Machines, and identifies pressing challenges and avenues for future research. It delves into the applications of AI in NLP, showcasing its transformative potential in tasks such as machine translation, facilitated by deep learning methods, and the development of chatbots and virtual assistants that have revolutionized human-technology interaction. The paper also highlights other fields impacted by AI techniques, including text summarization, sentiment analysis, and named entity recognition, emphasizing the efficiency and accuracy brought about by the integration of AI in these areas. In conclusion, the paper summarizes the remarkable advancements and persistent challenges in NLP, such as language ambiguity and contextual understanding, and underscores the need for diverse and representative labeled data for training. Looking forward, it identifies promising research avenues including Explainable AI, Few-shot and Zero-shot Learning, and the integration of NLP with other data modalities, aiming for a holistic understanding of multimodal data. The paper calls for enhanced robustness and security in NLP systems, especially in sensitive applications like content moderation and fake news detection, to foster trust and reliability in AI technologies. It advocates for continual learning in NLP models to adapt over time without losing previously acquired knowledge, paving the way for a future where AI and NLP work synergistically to understand and generate human language more effectively and efficiently.

The rapid advancement of technology, particularly artificial intelligence (AI), is transforming the world of management. This chapter delves into the futuristic trends in management, focusing on the integration of AI into various managerial aspects, its potential benefits, challenges, and strategies for successful adoption. AI-driven decision-making aids managers in data-driven processes, identifying patterns, trends, and insights through AI algorithms. AI's impact on human resources management includes AI-enabled talent acquisition and recruitment, enhanced employee experience through AI-driven content, and AI-driven operations management. AI-driven supply chain optimization, process automation, and customer relationship management (CRM) are also explored. However, ethical implications of AI in management include addressing biases and fairness concerns, ensuring transparency and accountability, and navigating privacy and data security challenges. Managing the human-AI collaboration involves building a culture that embraces AI while valuing human expertise, fostering a learning mindset, encouraging continuous skill development, and mitigating potential job displacement and promoting AI-human synergy. The chapter emphasizes the importance of fostering a learning mindset, encouraging continuous skill development, and mitigating potential job displacement. AI's integration into management practices has the potential to revolutionize various aspects of organizations, including data management, resource allocation, personalization, risk assessment, supply chain management, and employee productivity. AI-driven tools enable efficient data management, identification of trends, correlations, and actionable insights from complex datasets. They can optimize financial resources, human capital, or physical assets, enhance operational efficiency, and provide personalized customer experiences. AI-driven decision-making aids managers in making informed decisions by leveraging capabilities such as data processing, pattern recognition, real-time insights, and predictive analytics. These capabilities help managers segment customers, analyze market trends, and predict future demand. AI also enhances predictive and prescriptive analytics by providing recommendations for optimal performance. AI's impact on human resources management includes AI-enabled talent acquisition and recruitment. AI-powered tools can streamline the traditional recruitment process by scanning online platforms, screening resumes, and conducting assessments and skill evaluations. AI helps mitigate bias in the hiring process through blind hiring, objective evaluation, and data-driven decisions. However, challenges and ethical considerations include data privacy and security, transparency and explainability, algorithmic bias, and candidate experience. In conclusion, AI's integration into management practices has the potential to revolutionize various areas, including data management, resource allocation, personalization, risk assessment, supply chain management, and employee performance. However, HR professionals must address ethical concerns such as data privacy, transparency, algorithmic bias, and candidate experience to ensure the success and competitiveness of AI-based recruitment. AI can significantly enhance the employee experience by providing personalized learning and development programs, enhancing performance evaluations, building employee engagement strategies, and predicting potential attrition risks. AI-powered tools can assess employees' existing skills, knowledge gaps, and learning preferences, enabling the creation of personalized development plans. AI-driven learning platforms can adjust the difficulty and content of training materials based on individual progress, fostering a culture of continuous learning. AI-based performance evaluations can provide valuable insights to enhance the performance evaluation process, offering real-time tracking and 360-degree feedback analysis. AI-driven employee engagement strategies can bolster employee satisfaction, tailoring benefits, rewards, and recognition programs to individual needs. Predictive attrition analysis and chatbots for employee support can also help retain valuable employees. AI can also optimize supply chain operations by accurately predicting demand and managing inventory. AI-driven systems can dynamically adjust inventory levels based on real-time demand fluctuations and lead times, maintaining optimal resource utilization and minimizing excess inventory. AI-powered logistics and route optimization can revolutionize logistics and transportation management by optimizing routes and enhancing overall efficiency. AI-driven process automation can transform various business operations by identifying suitable processes, analyzing high-volume data processing, streamlining workflow and resource allocation, and addressing workforce concerns and upskilling needs amid automation. By embracing AI in operations management, businesses can achieve unprecedented levels of efficiency, resilience, and responsiveness to market demands. In conclusion, AI can transform the employee experience, drive productivity, and foster a dynamic workforce. However, careful consideration of ethical principles and continuous monitoring of AI systems are essential for responsible implementation. AI can significantly improve customer satisfaction and loyalty through real-time feedback analysis, chat sentiment analysis, and personalized loyalty programs. AI-powered customer support includes chatbots and virtual assistants, which provide instant and round-the-clock assistance. AI platforms, such as Natural Language Processing (NLP), enable chatbots to understand and respond to customer queries in a conversational manner. However, AI integration also presents challenges, such as addressing biases and fairness concerns in AI algorithms, ensuring transparency and accountability in AI systems, and managing privacy and data security challenges. Adhering to data protection regulations and implementing robust data governance frameworks are essential for safeguarding customer and organizational data. Managing the human-AI collaboration involves building a culture that embraces AI while valuing human expertise. This can be achieved through change management and training, promoting a collaborative environment, fostering a learning mindset, upskilling and reskilling initiatives, emphasizing creativity and critical thinking, and mitigating potential job displacement and promoting AI-human synergy. In conclusion, embracing AI in management offers numerous opportunities to enhance organizational efficiency, productivity, and competitiveness. It is crucial for management professionals and corporate leaders to understand and harness the power of AI responsibly. By being proactive in addressing challenges and aligning AI initiatives with organizational values, businesses can leverage futuristic trends in management to thrive in the dynamic and ever-evolving landscape.

In the rapidly evolving landscape of digital technology, the proliferation of interconnected systems has brought unprecedented opportunities and challenges. Among these challenges, the escalating frequency and sophistication of cyberattacks pose significant threats to individuals, organizations, and nations. In response, the fusion of Cybersecurity and Artificial Intelligence (AI) has emerged as a pivotal paradigm, offering proactive, intelligent, and adaptable defense mechanisms. This research explores the transformative impacts of AI-powered security on cybersecurity, demonstrating how AI techniques, including machine learning, natural language processing, and anomaly detection, fortify digital infrastructures. By analyzing vast volumes of data at speeds beyond human capacity, AI-driven cybersecurity systems can identify subtle patterns indicative of potential threats, allowing for early detection and prevention. The exploration consolidates existing studies, highlighting the trends and gaps that this research addresses. Expanded results and discussions provide a detailed analysis of the practical benefits and challenges of AI applications in cybersecurity, including case studies that offer concrete evidence of AI's impact. Novel contributions are emphasized through comparisons with other studies, showcasing improvements in accuracy, precision, recall, and F-score metrics, which demonstrate the effectiveness of AI in enhancing cybersecurity measures. The synergy between AI and human expertise is explored, highlighting how AI-driven tools augment human analysts' capabilities. Ethical considerations and the "black box" nature of AI algorithms are addressed, advocating for transparent and interpretable AI models to foster trust and collaboration between man and machine. The challenges posed by adversarial AI, where threat actors exploit AI system vulnerabilities, are examined. Strategies for building robust AI security mechanisms, including adversarial training, model diversification, and advanced threat modeling, are discussed. The research also emphasizes a holistic approach that combines AI-driven automation with human intuition and domain knowledge. As AI continues to rapidly evolve, a proactive and dynamic cybersecurity posture can be established, bolstering defenses, mitigating risks, and ensuring the integrity of our increasingly interconnected digital world.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

Artificial intelligence (AI) has had a significant impact on the accounting industry by improving accuracy, automating repetitive tasks, and increasing efficiency. In addition to financial automation, auditing, tax compliance, and predictive analysis, advanced AI technologies like machine learning (ML), robotic process automation (RPA), and natural language processing (NLP) are becoming more and more integrated into various accounting functions. These innovations enable businesses to reduce operational costs and help more educated and appropriate decision-making. Despite its benefits, AI still has some disadvantages. Worries about different problems still persist, including the “black box” problem, the lack of transparency in AI decision-making, job displacements, threats to data security, and professional limitations. This report examines the benefits and drawbacks of AI for the accounting industry. Additionally, it looks at how accountants can reevaluate their roles, effectively integrate AI-driven tools, and acquire new skills. Organizations should strike a balance between AI techniques and human expertise in order to maximize the benefits of AI while reducing its challenges. This report addresses the importance of ensuring AI applications adhere to market standards and ethical requirements while supporting compliance.

Artificial Intelligence Solutions to Improve Emergency Department Wait Times: Living Systematic Review.

Readiness for Artificial Intelligence in Biobanking

Introduction: this review paper provides a comprehensive examination of the applications and impact of artificial intelligence (AI) in the field of education. With advancements in AI technologies, the educational landscape has witnessed significant transformations. This review aims to explore the diverse AI techniques employed in education and their potential contributions to teaching, learning, assessment, and educational support. Objective: this research article aims to tracing the development of AI in education from its early beginnings to its current state. It highlights key milestones and breakthroughs that have shaped the field, including the emergence of intelligent tutoring systems and expert systems. Methods: the article provides a comprehensive overview of the various AI techniques utilized in education, such as machine learning, natural language processing, computer vision, and data mining. Each technique is discussed in detail, showcasing the algorithms, models, and methodologies used within each approach. Results: while the benefits of AI in education are substantial, the paper also addresses the challenges associated with its integration. Ethical considerations, privacy concerns, and the need for effective human-AI collaboration are discussed in-depth. Conclusion: this review underscores the transformative potential of AI in education. By harnessing AI technologies effectively and responsibly, educators and policymakers can unlock new possibilities for enhancing teaching and learning experiences, fostering personalized instruction, and driving educational advancement.

Artificial Intelligence (AI) is increasingly transforming healthcare by enhancing diagnostic accuracy, personalizing treatment, and improving operational efficiencies. This review explores AI's impact across several key areas: cancer medicine, fraud detection, and lessons from the petroleum industry. In cancer medicine, AI-driven advancements are leading to more accurate diagnostics, personalized treatment plans, and predictive models for patient outcomes. In fraud detection, AI techniques such as anomaly detection and natural language processing are effectively identifying and mitigating fraudulent activities, safeguarding financial and operational integrity. Insights from the petroleum industry reveal how AI applications, such as predictive maintenance and operational optimization, can be adapted to healthcare settings to enhance equipment reliability and resource management. Emerging trends include the integration of AI with genomics, telemedicine, and cross-disciplinary innovations, which promise further advancements in personalized care and operational efficiency. However, ethical considerations such as data privacy, bias, and transparency must be addressed to ensure responsible AI deployment. The review concludes by highlighting the need for continued innovation, collaboration, and patient-centric approaches to fully realize AI's potential in transforming healthcare and improving patient outcomes.

This research paper examines the influence of Artificial Intelligence (AI) on the efficiency of project management, concentrating on the ways in which AI tools and techniques improve different facets of project management. The study initiates with a comprehensive examination of project management and the increasing significance of AI in various domains, emphasizing the shortcomings of conventional methodologies and the capacity of AI to tackle these issues. The study seeks to evaluate the impact of AI on project processes, pinpoint effective AI tools, and investigate the advantages and obstacles associated with AI integration. A thorough examination of existing literature investigates AI tools, including machine learning, natural language processing, and predictive analytics, along with their applications in planning, scheduling, resource allocation, risk management, communication, and performance monitoring. Case studies and empirical evidence illustrate the beneficial effects of AI on project outcomes, while also highlighting existing gaps in the literature that warrant further investigation. The mixed-methods approach integrates qualitative and quantitative data sourced from surveys, interviews, and case studies, employing both thematic and statistical analysis. The findings indicate that AI plays a crucial role in enhancing project efficiency through the reduction of timelines, improvement of cost control, optimization of resources, and facilitation of proactive risk management, in addition to enhancing communication and collaboration. Nonetheless, factors such as initial investment, skill requirements, resistance to change, and ethical considerations are acknowledged. The discussion outlines actionable strategies for project managers, highlighting the importance of training, incremental integration, ensuring data quality, and promoting innovation. The study concludes by offering recommendations for future research regarding the long-term effects of AI, conducting cross-industry analyses, and creating AI tools specifically designed for project management. This approach aims to provide empirical evidence and strategic insights into the integration of AI for enhanced project outcomes.

Artificial intelligence has recently become a rapidly growing global trend, given its advanced technological capabilities and capabilities and its ability to perform tasks that were previously dependent on humans. This makes the use of AI particularly beneficial in the tourism and travel industry, saving tourism organizations time, effort, and costs. It also eliminates, or at least reduces, human error, allowing tasks to be performed quickly, at any time, and around the clock, thus achieving customer satisfaction. The tourism industry, with all its components, from tourism companies to hotels, is committed to providing exceptional, high-quality customer service to achieve customer satisfaction. In this area, artificial intelligence technologies help achieve customer satisfaction in multiple ways. These include, for example, the use of AI in tourism and travel to provide a more personalized tourism experience, ensure rapid response to customer requests, and implement voice assistance, chat, and smart marketing, in addition to providing virtual tourism experiences, augmented and integrated reality, and assist in forecasting, data analysis, and problem-solving. From this standpoint, the study aimed to identify the nature of artificial intelligence and its importance in the tourism and travel industry in Iraq and to address artificial intelligence techniques in the tourism and hospitality sector and the challenges facing it. The study also addresses the concept of customer satisfaction, the importance of achieving it, and methods of measuring customer satisfaction in the tourism sector through the service quality model. The study relied on the descriptive analytical approach and used the questionnaire to collect study data from two random samples: the first of customers in tourism companies in the city of Karbala, amounting to (70) individuals, and the second of customers of premium hotels in the city of Karbala, amounting to (90) individuals. The research results concluded that there is a relationship between artificial intelligence and customer satisfaction in the tourism and travel industry in Iraq.

Artificial Intelligence (AI) has transformed literary criticism by introducing advanced tools to analyze complex literary elements such as themes, symbolism, and emotional depth. This chapter explores AI's impact on both contemporary and classic literature, demonstrating how Natural Language Processing (NLP) and machine learning algorithms uncover recurring motifs, narrative structures, and cultural variations beyond human perception. AI's role in interpreting symbolism and identifying thematic connections across genres is examined, alongside its limitations in capturing abstract meanings compared to human intuition. The chapter also investigates AI's effectiveness in assessing emotional resonance using sentiment analysis and tonal evaluation to measure audience engagement. Ultimately, this chapter presents AI as both an innovative tool and a disruptive force, reshaping literary analysis in the digital age.

Artificial Intelligence (AI) has become a transformative force in education, significantly influencing students. This research explores AI's impact on learning experiences, academic performance, career guidance, motivation, self-reliance, social interaction, and AI dependency. Utilizing a descriptive-comparative design, 194 student respondents were chosen through stratified sampling. The results show that students generally perceive AI positively. Students agree that AI enhances personalized learning, engagement, and critical thinking, although practical hands-on learning experiences received less favorable feedback. Academically, students concur that AI helps them identify weaknesses, improve assignments, and track progress, despite some reservations about its efficacy in exam preparation. For career guidance, students agree that AI effectively matches skills with career options, recommends internships, and provides resources, though it is less effective for long-term planning. Students also believe AI boosts motivation through gamified learning and progress tracking and fosters self-reliance via self-directed learning and critical thinking support. Socially, students agree that AI facilitates collaboration, peer learning, and networking. Additionally, students demonstrate a reliance on AI for their learning processes. Notably, female students report a more significant impact on social interactions than male students. The type of device used (laptop vs. cellphone) significantly affects the learning experience, with laptop users reporting a more substantial impact. Differences in AI's impact are noted among various courses, particularly benefiting education students more than those in hospitality management and agriculture. However, age and family income do not significantly influence AI's overall impact. Keywords: Artificial Intelligence, descriptive-comparative, impact on learning, higher education

This paper explores the evolution and impact of artificial intelligence (AI) in the realm of language technologies. We trace the historical development of language models in AI, starting from the rule-based systems of the 1960s to the sophisticated neural networks of today. The current state-of-the-art technologies, particularly transformer-based models like OpenAI's GPT series, are examined for their capabilities and limitations. We delve into the role of AI in language acquisition and learning, highlighting AI-driven language teaching tools such as Duolingo and Babbel, and discuss their effectiveness and challenges. Furthermore, the paper explores the significant contributions of AI in second language acquisition research, including the development of predictive models and sophisticated learner profiles. Ethical considerations and challenges, such as data privacy and potential biases, are also addressed. We discuss advancements in natural language processing (NLP) applications like text and sentiment analysis, speech recognition and generation, and machine translation, along with their cross-linguistic challenges. The conclusion envisions future directions for AI in language technologies, emphasizing the need for multimodal inputs, efficiency, and enhanced interpretability.

Artificial intelligence (AI) has had a profound impact on various industries, and one prominent domain where its influence is evident is in chatbot technology. Chatbots, computer programs designed to simulate human conversation, have evolved significantly through the advancements in AI, becoming more sophisticated and intelligent. This research paper aims to explore the current state of AI-powered chatbot technology, focusing on the latest advancements and leading innovations. The study delves into the application of natural language processing (NLP) algorithms, machine learning models, and deep learning techniques in chatbot development to gain insights into their capabilities and limitations. The research also highlights leading innovations in AI-powered chatbot technology, such as virtual assistants and voice-enabled chatbots. These conversational agents have transformed various industries, providing innovative solutions to virtual reference services and customer-company interactions. The study delves into the contextual understanding and personalized responses that chatbots can provide, offering tailored interactions to meet users' specific needs and preferences. Furthermore, the integration of other technologies, including speech recognition and sentiment analysis, enhances chatbot capabilities, improving user satisfaction and engagement. However, while AI-powered chatbots have enhanced user experiences, customer satisfaction, and efficiency in industries like customer support and service, they also raise potential ethical and privacy concerns. Medical chatbots, in particular, pose legal and ethical challenges that require careful management and the development of appropriate ethical frameworks. Understanding the advancements, innovations, and impact of AI on chatbot technology is essential for recognizing the potential benefits and challenges these systems present. By addressing ethical and privacy concerns, chatbots can responsibly shape the future of human-computer interactions, further contributing to the broader understanding of AI's role in transforming industries and enhancing user experiences.