AI-powered Systems Research Articles

The Role of Technology and Automation in Streamlining Business Processes and Productivity for SMEs

Purpose: This research investigates the vital role of automation in enhancing business process efficiency and overall productivity within SMEs. It also discusses how technology can help overcome bottlenecks arising from human operation, resource limitation, and operational inefficiency, which have commonly been experienced by SMEs. Materials and Methods: The study combines an extensive review of existing literature with case studies from various industries to evaluate the effects of automation tools on SME operations. These tools range from cloud-based solutions to AI-powered systems, providing insights into their impact across different business processes. Findings: Research has shown that automation improves productivity in SMEs by up to 30%, reduces manual errors by 25%, and enhances data accuracy by improving employee focus on strategic tasks. In addition, the standardization of processes through automation ensures less variability in output. Automation also impacts customer experience positively by ensuring speed in service delivery and quality improvement in products. High upfront costs, however, combined with a shortage in skilled personnel and integrating new technologies with existing systems, remain barriers to wider adoption by SMEs. Implications to Theory, Practice and Policy: Accordingly, the study recommends that the following measures be taken to counteract the problems posed: phase in automation, cooperate with technology providers who will devise cost-effective solutions, and invest in comprehensive training programs to fill the skills gap. Government incentives, such as tax breaks and subsidies, could also push SMEs toward broader automation. Thereafter, successful integration of automation will enhance the competitiveness of SMEs and create a sustainable growth path in the digital economy.

AI-Powered customer experience optimization: Enhancing financial inclusion in underserved communities

The integration of Artificial Intelligence (AI) into customer experience optimization holds transformative potential for advancing financial inclusion in underserved communities. This review explores how AI-driven tools can enhance personalized banking services, streamline customer engagement, and improve access to financial products for individuals who are often marginalized by traditional banking systems. By leveraging AI algorithms, financial institutions can analyze vast amounts of customer data to identify unique needs, preferences, and behaviors, enabling the delivery of tailored financial solutions. These AI-powered systems can also automate routine tasks, such as account management and customer support, ensuring that banking services are more accessible, efficient, and cost-effective. A key benefit of AI in this context is its ability to overcome barriers such as geographic isolation and lack of financial literacy, which often hinder financial inclusion. AI-driven chatbots, for instance, can provide 24/7 support in local languages, guiding users through complex financial processes. Moreover, AI enables the creation of personalized financial literacy programs, tailored to the specific needs of underserved populations, fostering a deeper understanding of financial products and encouraging greater participation in the financial system. Additionally, AI-powered fraud detection systems enhance trust and security in financial transactions, a critical factor in encouraging participation from communities traditionally skeptical of formal banking systems. The use of predictive analytics further supports the development of micro-loans and credit products suited to the financial realities of low-income individuals, reducing default rates and improving access to capital. In conclusion, AI-powered customer experience optimization offers a powerful strategy for financial institutions seeking to expand their reach into underserved communities. By providing personalized, efficient, and secure banking experiences, AI can significantly contribute to financial inclusion efforts, driving economic empowerment and reducing inequalities. This study highlights the critical role of AI in reshaping financial services and creating inclusive opportunities for underserved populations. Keywords: Artificial Intelligence, Financial Inclusion, Customer Experience Optimization, Underserved Communities, Personalized Banking, AI-Powered Tools, Financial Literacy, Fraud Detection, Predictive Analytics, Economic Empowerment.

Artificial Intelligence to support Children with Autism

Children with autism spectrum disorder (ASD) face challenges in communication, social interaction, and behavioral flexibility, which can affect their development and everyday functioning. Artificial intelligence (AI) offers innovative tools to support children with autism by providing personalized and adaptive interventions. AI-powered systems, such as robots, virtual assistants, and machine learning models, can be used to enhance social skills training, improve communication, and manage sensory sensitivities. These technologies can monitor progress, adjust strategies, and provide real-time feedback, enabling more effective, individualized care. This paper explores the potential of AI in autism support, focusing on AI applications for social skill enhancement, early detection, and therapeutic intervention, while also addressing the ethical considerations and challenges involved in integrating AI into autism care.

Application of Artificial Intelligence for Library Services: A Systematic Literature Review

Artificial intelligence (AI) revolutionizes traditional library services by offering opportunities for effective and quality service delivery through AI-powered systems. Artificial intelligence has the potential to automate many library operations and services to provide 24/7 assistance to library users in meeting their information requirements. Hence, libraries tend to integrate artificial intelligence into their operations and service delivery. This can be further verified by reviewing the current state of knowledge regarding artificial intelligence and libraries. Therefore, the objectives of the study were to find the current status of knowledge regarding the application of artificial intelligence for library services and to investigate the research gaps in the application of artificial intelligence for library services. The study followed the Systematic Literature Review (SLR) methodology and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.The review revealed that extensive research studies were focused on discovering different library services and operations where AI can be utilized to enhance the effectiveness and efficiency of service delivery, and challenges faced by libraries in using AI tools. The study further recognized areas where studies are lacking which include different AI tools for library service delivery, smart libraries, natural language processing, AI-powered Chatbots, ethical considerations of AI, education and personalized learning, digital library and Distributed Artificial Intelligence, AI for participatory library service, future of libraries in the age of AI, AI Literacy. Hence, the findings provide some implications for future research regarding artificial intelligence and libraries.

Revolutionizing Cybersecurity with AI: Predictive Threat Intelligence and Automated Response Systems

The sophistication and breadth of cyber threats are continuously expanding, making it more difficult for traditional security measures to keep up. Artificial intelligence is revolutionizing cybersecurity by equipping businesses to proactively counter threats with automated reaction systems and predictive threat intelligence. Data analytics, behavioral analysis, and machine learning enable AI-powered systems to anticipate cyber assaults, enabling more efficient and rapid threat detection. By automating reaction mechanisms and mitigating threats in real-time, AI systems can minimize human error and maximize damage mitigation. AI techniques, such as anomaly detection, predictive modeling, and real-time threat analysis; data privacy, ethics, and the risks of hostile attacks are among the subjects covered, as are the benefits and drawbacks of utilizing AI in cybersecurity. This article provides the framework for future intelligent, automated cyber defense methods and illustrates how AI may alter cybersecurity using real-life examples and case studies.

Explainable AI for Transparent and Trustworthy Tuberculosis Diagnosis: From Mere Pixels to Actionable Insights

Building transparent and trustworthy AI-powered systems for disease diagnosis has become more paramount than ever due to a lack of understanding of black box models. A lack of transparency and explainability in AI-driven models can propagate biases and erode patients' and medical practitioners' trust. To answer this challenge, Explainable AI (XAI) is drastically emerging as a practical solution and approach to tackling ethical concerns in the health sector. The overarching purpose of this paper is to highlight the advancement in XAI for tuberculosis diagnosis (TB) and identify the benefits and challenges associated with improved trust in AI-powered TB diagnosis. We explore the potential of XAI in improving TB diagnosis. We attempt to provide a complete plan to promote XAI. We examine the significant problems associated with using XAI in TB diagnosis. We argue that XAI is critical for reliable TB diagnosis by improving the interpretability of AI decision-making processes and recognising possible biases and mistakes. We evaluate techniques and methods for XAI in TB diagnosis and examine the ethical and societal ramifications. By leveraging explainable AI, we can create a more reliable and trustworthy TB diagnostic framework, ultimately improving patient outcomes and global health. Finally, we provide thorough recommendations for developing and implementing XAI in TB diagnosis using X-ray imaging

The Evolution of Data Centers in the Age of AI

This article explores the evolving landscape of data centers in the era of artificial intelligence (AI). It examines the exponential growth of the global data center market, driven by increasing data generation and AI adoption. The article discusses key technological developments in data centers, including enhanced operational efficiency through AI-powered systems, specialized hardware for AI workloads, advanced cooling technologies, and sustainability initiatives. It also delves into future prospects, such as increased capacity for complex AI tasks, real-time processing of massive datasets, and further improvements in energy efficiency. The symbiotic relationship between AI and data centers is highlighted, emphasizing how this transformation is reshaping digital infrastructure to meet unprecedented demands for computational power and data processing while striving for sustainability.

Evaluating the Impact of Artificial Intelligence in Managing Construction Engineering Projects

This study evaluates the impact of utilizing artificial intelligence (AI) in managing construction engineering projects. With the increasing complexity and scale of construction projects, AI offers promising solutions to enhance efficiency, accuracy, and decision-making processes. The study investigates the potential benefits, challenges, and practical applications of AI through detailed case studies. The study employs a mixed-methods approach, combining qualitative and quantitative research methods. Data was collected through literature reviews and case studies where AI had been successfully implemented. The analyses included comparisons between projects that used AI and those that did not. The findings demonstrated significant improvements in project efficiency, cost estimation accuracy, and risk management. For instance, AI-powered systems reduced scheduling errors by 35%, leading to more accurate timelines. Additionally, the integration led to a 20% reduction in project durations due to improved resource allocation and proactive risk management. Furthermore, AI-supported systems contributed to a 25% improvement in stakeholder satisfaction. In terms of cost estimation, AI-powered estimation tools improved the accuracy of cost estimates by 30%, helping to reduce budget overruns by 40%. In risk management, AI-supported tools enhanced the accuracy of risk identification by 45%, leading to the early detection of potential issues and the development of effective mitigation strategies that reduced the impact of risks by 30%. Thanks to these improvements, project success rates increased by 20%. These results demonstrate that integrating AI into the management of engineering construction projects can lead to tangible improvements in project efficiency, cost accuracy, and risk management, thereby enhancing stakeholder satisfaction and contributing to more successful project outcomes.

AI-Driven FlightOps: Revolutionizing Airline Operations Control with Predictive Analytics and NLP

Abstract: Flight operations control centers (OCCs) are the nerve centers of airline operations, tasked with managing a wide range of activities such as flight scheduling, crew assignments, maintenance coordination, and dealing with disruptions. While traditional approaches have relied heavily on human decision-making and manual analysis of operational data, the increasing complexity of modern airline operations demands more efficient solutions. This paper presents FlightOps AI, a cutting-edge AIpowered system that integrates advanced natural language processing (NLP), deep neural networks (DNNs), reinforcement learning (RL), and computer vision to provide OCC staff with actionable insights and predictive capabilities. The system processes massive amounts of structured and unstructured data in real time, enabling smarter decision-making and automation of routine tasks. By adopting these technologies, FlightOps AI transforms the operational landscape, optimizing airline performance while minimizing human errors

Deployment of Industry 4.0 into the Agricultural Food Industry: A Focus on Facet, Insight, Knowledge, and Resilience (FIKR) Personality Traits and AI-Powered Inventory Management

Integrating Artificial Intelligence (AI) in precision agriculture within the framework of Industry 4.0 (I4) is revolutionizing crop disease management and inventory management, offering innovative solutions that enhance both agricultural productivity and environmental sustainability. Combined with I4 technologies, AI-powered systems can predict, detect, and manage crop diseases accurately, reducing reliance on chemical pesticides and improving overall farm efficiency. AI algorithms identify disease patterns and suggest optimal intervention strategies by analyzing real-time data from drones, sensors, and satellite imagery. This approach minimizes crop loss, maximizes yield, and aligns with sustainable farming practices by reducing the environmental footprint. However, the success of these technologies is influenced by the personality traits of farmers. Traits such as openness to innovation, conscientiousness, and analytical thinking are crucial for the effective adoption and utilization of AI-driven solutions. Conscientious farmers follow precise instructions and maintain equipment, while those open to new experiences are more likely to experiment with innovative technologies. Analytical thinkers excel in interpreting complex data, and making informed decisions that improve crop health and yield. The research underscores the need for fostering these traits among farmers to maximize the benefits of AI technologies. Additionally, the study highlights the importance of interdisciplinary collaboration in developing and implementing AI-driven solutions that address both agricultural productivity and environmental sustainability. By integrating technological advancements with human factors, AI has the potential to transform crop disease management, contributing to a more sustainable and resilient agricultural system. The findings call for continued research, policy support, and a holistic approach to fully realize the benefits of AI in agriculture.

Impact of Artificial Intelligence in Customer Journey

The entire gamut of Customer journey is undergoing a massive transformation due to the rapid advancement of Artificial Intelligence (AI). Leveraging the power of AI , CRM & systems have refined the aspect of how businesses manage and optimize the customer journey. AI-powered systems have significant impact across various stages of the customer lifecycle by use of techniques such as machine learning to empower businesses to use systems that can analyse vast amounts of customer dataset in real-time, enabling them to gain deeper insights in customer behaviours, preferences, & sentiment. The AI-driven techniques help businesses to drive more personalized & targeted marketing campaigns, tailored recommendations, and extend efficient customer service leading ultimately to enhancing customer satisfaction and loyalty. Moreover, AI-powered systems have capabilities of offering predictive analytics which empower businesses to forecast customer behaviours and anticipate their needs. The capabilities help businesses in effective resource optimization and improve efficiency. For customer service AI-powered chatbots and virtual assistants are used to enhance engagement by providing instant responses and ability to handle resolving issues promptly.

Application of Artificial Intelligence in Advanced Training and Education of Emergency Medicine Doctors: A Narrative Review

Emergency medicine (EM) demands continuous adaptation and refinement of training methodologies to equip healthcare professionals with the expertise to effectively manage complex and time-sensitive patient presentations. Artificial intelligence (AI), with its remarkable ability to process vast amounts of data, identify patterns, and make predictions, holds immense promise for enhancing the advanced training and education of EM physicians. This narrative review aims to discuss the potential of AI in transforming EM training and highlight the specific applications of AI in personalized learning, realistic simulations, data-driven decision support, and adaptive assessment, along with further exploring the benefits and challenges of AI-powered EM training. A comprehensive literature search was conducted using PubMed, MEDLINE, and Google Scholar to identify relevant studies focusing on AI applications in EM and EM training. The search terms included “artificial intelligence”, “emergency medicine”, “training”, “education”, “personalized learning”, “simulations”, “decision support”, and “assessment. Articles published in the past ten years were prioritized to ensure the inclusion of current advancements in the field. AI offers a plethora of opportunities to revolutionize EM training, including the following: Personalized learning: AI-powered systems can tailor educational content and pace to individual trainees’ needs, ensuring optimal instruction and knowledge acquisition. Realistic simulations: AI-powered simulations provide immersive experiences for trainees to practice clinical decision making under simulated pressure. Data-driven decision support: AI-powered systems analyze vast amounts of data to provide trainees with real-time recommendations and insights for informed clinical decisions. Adaptive assessment: AI-powered tools assess trainee progress dynamically, providing personalized feedback and identifying areas for improvement. Conclusions: AI integration into EM training holds immense promise for enhancing trainee learning and improving patient outcomes. By embracing AI, we can cultivate a new generation of EM physicians equipped to meet the ever-changing demands of this critical medical specialty.

Harnessing the Potential of Artificial Intelligence in Managing Viral Hepatitis

Viral hepatitis continues to be a serious global health concern, impacting millions of people, putting a strain on healthcare systems across the world, and causing significant morbidity and mortality. Traditional diagnostic, prognostic, and therapeutic procedures to address viral hepatitis are successful but have limits in accuracy, speed, and accessibility. Artificial intelligence (AI) advancement provides substantial opportunities to overcome these challenges. This study investigates the role of AI in revolutionizing viral hepatitis care, from early detection to therapy optimization and epidemiological surveillance. A comprehensive literature review was conducted using predefined keywords in the Nature, PLOS ONE, PubMed, Frontiers, Wiley Online Library, BMC, Taylor & Francis, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, and Google Scholar databases. Peer-reviewed publications written in English between January 2019 and August 2024 were examined. The data of the selected research papers were synthesized and analyzed using thematic and narrative analysis techniques. The use of AI-driven algorithms in viral hepatitis control involves many significant aspects. AI improves diagnostic accuracy by integrating machine learning (ML) models with serological, genomic, and imaging data. It enables tailored treatment plans by assessing patient-specific characteristics and predicting therapy responses. AI-powered technologies aid in epidemiological modeling, and AI-powered systems effectively track treatment adherence, identify medication resistance, and control complications associated with chronic hepatitis infections. It is vital in identifying new antiviral medicines and vaccines, speeding the development pipeline through high-throughput screening and predictive modeling. Despite its transformational promise, using AI in viral hepatitis care presents various challenges, including data privacy concerns, the necessity for extensive and varied datasets, and the possibility of algorithmic biases. Ethical considerations, legal frameworks, and multidisciplinary collaboration are required to resolve these issues and ensure AI technology’s safe and successful use in clinical practice. Exploiting the full AI’s potential for viral hepatitis management provides unparalleled prospects to improve patient outcomes, optimize public health policies, and, eventually, and alleviate the disease’s negative impact worldwide. This study seeks to provide academics, medics, and policymakers with the fundamental knowledge they need to harness AI’s potential in the fight against viral hepatitis.

AI-Powered System for Optimizing Irrigation in Precision Agriculture

AI-Powered System for Optimizing Irrigation in Precision Agriculture

AI-Driven Threat Detection and Response Systems for Secure National Infrastructure Networks: A Comprehensive Review

Abstract: Due to the increased complexity and damage of cyberattacks in this digital age, the security of national infrastructure networks has become a vital concern. However, a possible approach to improve the cybersecurity of these crucial networks is to incorporate artificial intelligence (AI) into threat detection and response systems; to rapidly evaluate large data sets, identify anomalies, and automate countermeasures to lessen the effects of cyberattacks. The impact, implementation and approaches for anomaly detection and response automation of AI-powered solutions for safeguarding national infrastructure are examined in this paper. Understanding how AI technologies are used to automate threat detection and response, reviewing the operational usefulness of AI in enhancing cybersecurity measures and evaluating the deployment of these systems in critical infrastructure settings were also examined. The study revealed that the speed and accuracy of threat detection and response are greatly increased by AI-powered systems. The automation capacity of AI can potentially reduce the need for human analysts, while also providing faster threat mitigation. Additionally, the usefulness of AI across sectors indicates its practicality in situations and how it may adapt in response to new threats. In conclusion, AI-driven threat detection and response systems are an important development in national infrastructure network cybersecurity. Therefore, by improving the capacity to recognize and address cyber-attacks these technologies can ultimately increase the overall resilience of national infrastructures.

AI integration in business development: Ethical considerations and practical solutions

The winds of change are sweeping through the business landscape, propelled by the transformative power of artificial intelligence (AI). This integration isn't merely a technological upgrade; it's a paradigm shift, unlocking a treasure trove of opportunities for companies to achieve unprecedented growth, efficiency, and innovation. Imagine AI-powered systems that analyze vast amounts of data, identify new market opportunities, predict customer behavior with uncanny accuracy, and even automating tedious tasks associated with lead generation and qualification. This is the future that AI promises, a future brimming with potential. However, the path to this future is not without its challenges. The immense power of AI comes with a critical responsibility: ensuring its ethical and responsible use. This review paper delves into the ethical considerations that businesses must navigate as they integrate AI into their business development strategies, exploring potential pitfalls like bias in algorithms, data privacy concerns, and job displacement. But fear not, for this paper doesn't just identify challenges; it also proposes practical solutions to navigate these complexities effectively. By acknowledging the potential for bias in AI algorithms and taking proactive steps to mitigate it, businesses can ensure fairness and inclusivity in their AI-driven decision-making. Imagine a team of data scientists meticulously scrutinizing training data, removing skewed information that could lead to discriminatory outcomes in lead generation or customer targeting. This commitment to responsible data practices fosters trust and ensures AI is used for good. Data privacy is another paramount concern in the age of AI. As AI systems collect and analyze vast amounts of customer data, businesses have a responsibility to safeguard this information. Imagine robust data security measures in place, protecting sensitive customer data with encryption protocols and access controls. Furthermore, fostering transparency about data collection practices and obtaining explicit consent from customers builds trust and demonstrates a commitment to responsible data stewardship. The specter of job displacement also looms large in the discussion of AI. While automation powered by AI might replace some repetitive tasks, it's important to remember that it also creates new opportunities. Imagine a world where AI handles lead scoring and qualification, freeing up human business developers to focus on high-value activities like strategic relationship building and closing deals. By investing in retraining programs and fostering a culture of continuous learning, businesses can empower their workforce to adapt and thrive in this evolving landscape.

AT THE CROSSROADS OF TECHNOLOGY AND MEDICINE: PROSPECTS OF AUTOMATION IN MEDICAL PRACTICE WITH THE USE OF NEURAL NETWORKS

This article delves into the exciting possibilities that arise when technology is applied to the field of medicine. It explores how automation can streamline various processes, leading to more efficient and accurate healthcare. One focus area is the potential for replacing direct interaction between doc-tors and patients with automated systems. By utilizing advanced neural networks, diagnostic and treatment processes can becomautomated, leading to faster and more precise outcomes. For exam-ple, AI-powered systems can analyze medical images and detect abnormalities with incredible accu-racy, aiding doctors in making diagnoses. Additionally, virtual assistants and chat bots can provide personalized healthcare advice, reducing the need for in-person consultations for minor concerns. These advancements not only enhance efficiency but also have potential to improve access to healthcare, especially in remote areas. Embracing technology in medicine opens up a lot of possibilities for better patient care and overall healthcare outcomes.

Ethical Considerations in the Development and Deployment of AI-Powered Systems

Ethical Considerations in the Development and Deployment of AI-Powered Systems

WITHDRAWN: Artificial-intelligent-powered safety and efficiency improvement for integrated railway systems

The multi-mode integrated railway system, anchored by the high-speed railway, caters to the diverse travel requirement both within and between cities, offering safe, comfortable, punctual, and eco-friendly transportation services. With the expansion of the railway networks, enhancing the efficiency and safety of the comprehensive system has become a cricual issue in the advanced development of railway transportation. In light of the prevailing application of artificial intelligence technologies within railway systems, this study leverages large model technology characterized by robust learning capabilities, efficient associative abilities, and linkage analysis analysis, to propose an AI-powered railway control and dispatching system. This system is elaborately designed with four core functions, including galobal optimum unattended dispatching, synergetic transportation in multiply modes, high-speed automatic control, and precise maintenance decision and execution. The deployment pathway and essential tasks of the system is further delineated, alongside the challenges and obstacles encountered. The AI-powered system promises a significant enhancement in the operational efficiency and safety of the composite railway system, ensuring a more effective alignment between transportation services and passenger demands.

Artificial Intelligence in Healthcare: Implications, Challenges, and Future Prospects

Artificial intelligence is a technology that enables machines, especially computer systems, to mimic human intelligence and problem-solving abilities. This technology is being used nowadays in many areas of the healthcare industry, from scheduling online appointments to robot-assisted, safer surgeries with better patient outcomes. Just like a physician, AI can record a patient's history, signs and symptoms, and lab results, leading to an accurate diagnosis and a proper treatment plan, but in no time. This is just a small example of the implications of AI in the sector. Other areas of medicine are also being benefited by this innovation, like gastroentology, radiology, surgery, and preventive medicine. The development of advanced algorithms has reduced the burden on radiologists by helping them identify abnormal images and pick up malignant lesions with minimal diagnostic errors. Similarly, AI-assisted colonoscopy can help identify malignant and benign polyps. AI-powered systems, like Google's DeepMind Health, may identify malignant growth in mammograms and diabetic retinopathy, which can aid in early detection and treatment.