Hyper-Personalization with AI and Edge Computing: The Future of Customer Experience in E-commerce and Retail

This article is aimed at studying the legal regulation of artificial intelligence and edge computing automated decision-making risks in wireless network communications. The data under artificial intelligence is full of flexibility and vitality, which has changed the way of data existence in the whole society. Its core is various algorithm programs, which determine the existence of artificial intelligence. In this environment, society develops rapidly with unstoppable momentum. However, from a legal perspective, artificial intelligence has algorithmic discrimination, such as gender discrimination, clothing discrimination, and racial discrimination. It does not possess openness, objectivity, and accountability. The consequences are sometimes serious enough to endanger the public interest of the entire society, leading to market disorder, etc. Therefore, the problem of artificial intelligence algorithm discrimination remains to be solved. This article uses algorithms to adjust algorithm discrimination to reduce the harm caused by artificial intelligence algorithm discrimination to a certain extent. First of all, this article introduces a regulatory-based edge cloud computing architecture model. It is mentioned that distributed cloud computing can use subsystems to calculate various resources and storage resources and can make automated decisions when calculating certain data. In order to reduce the impact of algorithm discrimination and trigger data diversification to reduce the probability of discrimination, an edge computing network data capture system is designed. And this article mentions the BP neural network model. The BP neural network model is divided into input layer, output layer, and hidden layer. The training samples are passed from the input layer to the output layer through the hidden layer. If the output information does not meet expectations, the error will be back-propagated, and the connection weight will be adjusted continuously. This paper proposes a deep learning system model in real-time artificial intelligence driven by edge computing. When this model is applied to legal regulations, it can cooperate with edge computing and artificial intelligence algorithms to provide high-precision automated decision-making. Finally, this paper designs an artificial intelligence-assisted automated decision-making experiment based on the theory of legal computing. This paper proposes a Bayesian algorithm that uses edge algorithms to merge into artificial intelligence and verifies the feasibility of this hypothesis through experiments. The experimental results show that it has a certain ability to regulate algorithmic discrimination caused by artificial intelligence in legal regulations. It can improve the regulatory effects of laws and regulations to a certain extent, and the improved artificial intelligence Bayesian algorithm clustering effect of edge computing is increased by about 7.2%.

The convergence of edge computing with Artificial Intelligence (AI) constitutes a watershed point in technological growth, with transformational implications for a wide range of sectors. This chapter explores the mutually beneficial interaction between edge computing and AI, as well as the inherent difficulties and broad implications for the direction of computing in the future. With its decentralized processing model, edge computing puts data analysis closer to the point of origin, facilitating improved efficiency and real-time insights. Edge devices enable sophisticated cognitive processes and autonomous decision-making through seamless integration with AI algorithms. However, this integration creates architectural challenges that call for creative solutions to handle privacy and security issues and strike a balance between processing power and other resources. The chapter examines how edge computing-enabled AI can revolutionize manufacturing, transportation, healthcare, and smart cities. It shows how AI can change predictive maintenance, individualized healthcare monitoring, and urban infrastructure optimization. New advances offer avenues for collaborative applications and decentralized AI training. Federated learning models, the spread of edge AI chips and algorithms, and the democratization of AI capabilities are some of these themes. In summary, this chapter provides direction on navigating the rapidly evolving area of artificial intelligence (AI) facilitated by edge computing, encouraging collaboration and innovation to fully achieve AI's transformative potential and address digital age concerns.

The integration of Artificial Intelligence (AI) in e-commerce platforms has significantly revolutionized how businesses optimize product performance and customer experiences. One of the most impactful applications of AI is in A/B testing, a method used to compare two versions of a webpage or product feature to determine which one performs better. AI-enabled A/B testing enhances this process by automating the experimentation, analyzing vast amounts of data in real-time, and providing actionable insights faster and more accurately than traditional methods. This review explores the impact of AI-enabled A/B testing on product performance in e-commerce platforms. AI-driven A/B testing algorithms can dynamically adapt tests based on user behavior, allowing for more personalized and effective product offerings. This leads to an increase in conversion rates, customer satisfaction, and ultimately, revenue. Furthermore, AI enables the testing of multiple variables simultaneously, which reduces the time required to identify optimal configurations and strategies. By leveraging machine learning models, e-commerce platforms can predict outcomes more accurately, ensuring that product changes are more likely to result in positive performance metrics. The review also discusses the challenges associated with implementing AI-enabled A/B testing, such as the need for large datasets, the risk of overfitting models to specific data segments, and the potential for bias in AI algorithms. Additionally, it highlights the importance of human oversight in interpreting AI-generated results to avoid misalignment with broader business goals. In conclusion, AI-enabled A/B testing represents a powerful tool for enhancing product performance in e-commerce platforms. It offers the ability to optimize user experiences in real-time, leading to more effective product strategies and higher customer engagement. As AI technology continues to advance, its role in A/B testing will likely expand, offering even greater opportunities for innovation in the e-commerce industry.. Keywords: Artificial Intelligence, A/B Testing, E-commerce, Product Performance, Conversion Rates, Machine Learning, Customer Engagement.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

Disclosure: H.M. Heshmati: None. N. Zameni: None.Background: Robotic surgery is quickly replacing traditional surgery performed by humans. It has significantly influenced the field of endocrine surgery. This review presents an update on the use of robotic surgery and artificial intelligence for adrenal tumors. Methods: A systematic search of literature was conducted using the search terms robotic surgery, artificial intelligence, adrenal tumors, safety, outcome, and cost. Results: Adrenal tumors are found in 10% of people who have imaging of the adrenal gland. They are typically unilateral and benign in the majority of cases. Robotic systems such as da Vinci Surgical System provide surgeons with enhanced dexterity and 3D visualization. Autonomous surgical robots can be categorized into various levels of autonomy including supervised autonomy (e.g., performance of specific tasks with human oversight), collaborative autonomy (e.g., robots working alongside human surgeons, sharing tasks and responsibilities), and fully autonomous systems (e.g., robots performing entire surgical procedures independently with human intervention when necessary). Robotic surgery allows access through tight spaces to remove adrenal tumors with small incision and minimal impact on surrounding tissues. The review of 1,162 patients who underwent adrenalectomy (robotic surgery in 747 patients, laparoscopic adrenalectomy in 415 patients) showed comparable blood loss and complications with the two techniques but patients with robotic surgery had significantly shorter hospital stay. The future of robotic surgery can be revolutionized by the integration of autonomous robots and artificial intelligence (AI), a field rapidly advancing. These innovations promise to further enhance the precision, safety, and effectiveness of surgery involving the adrenal glands. AI-powered systems provide real-time guidance and feedback to surgeons, enhancing decision-making and surgical precision. Autonomous systems can ensure consistent execution of surgical tasks, reducing variability and enhancing patient safety. AI algorithms can analyze extensive surgical data to refine techniques and predict potential complications, improving surgical outcomes. AI can customize surgical plans based on individual patient anatomy and pathology, potentially leading to better outcomes. The current high cost of these new procedures is a potential limitation for their use. Conclusion: Robotic surgery represents significant progress in minimally invasive techniques for the removal of adrenal tumors. It has improved dexterity and precision, and is associated with shorter recovery time compared to laparoscopic adrenalectomy. Collaboration between human surgeons and robotic systems can improve patient care and surgical outcomes. The precision, safety, and effectiveness of the adrenal surgery can be further enhanced by the integration of AI and robotics.Presentation: Saturday, July 12, 2025

The growing challenges of food security and climate change demand a rapid transformation in crop improvement strategies. Traditional breeding and crop development methods, while foundational, are often time-intensive and limited in their capacity to address the complexities of modern agricultural needs. Artificial intelligence (AI) has emerged as a disruptive technology, providing the tools to significantly accelerate crop improvement processes. This chapter delves into the integration of AI across the crop improvement pipeline, focusing on advancements that enhance the speed and precision of trait identification, genomic selection, and breeding optimization. Cutting-edge machine learning, deep learning, and reinforcement learning algorithms have enabled precise genotype–phenotype predictions and real-time decision-making. AI-powered high-throughput data integration, encompassing genomics, transcriptomics, and phenomics, accelerates the identification of stress-resilient genes and pathways. Furthermore, the application of AI in optimizing gene-editing workflows, designing CRISPR-based interventions, and automating phenotyping processes has dramatically shortened breeding cycles. The use of predictive models in speed breeding and climate-resilient crop development is also discussed, emphasizing the transformative potential of virtual environments and real-time analytics. Despite these advancements, challenges such as data quality, computational scalability, and ethical implications persist. The chapter concludes by exploring emerging trends like explainable AI, federated learning, and edge computing, underscoring their potential to revolutionize agricultural systems. By bridging technological innovations with sustainable practices, AI promises to redefine global agriculture, accelerating crop improvement to meet the growing demands of a rapidly changing world.

The fusion of wireless networks, the Internet of Things (IoT), and artificial intelligence (AI) holds transformative potential, ushering in an era where ubiquitous connectivity and intelligent decision-making reshape our world. This paper delves into the profound implications of this convergence, examining the interactions between wireless networks, IoT devices, and AI algorithms. Through an exploration of the evolution of wireless technologies, including the advent of 5G and beyond, the paper highlights the foundation upon which this convergence is built. The synergy of IoT, encompassing sensor-laden objects and data-driven insights, intertwines seamlessly with AI, empowering machines to emulate human cognition. The intricate relationships and dynamics between these paradigms unveil novel applications and unprecedented opportunities. From smart cities optimizing urban living to healthcare systems providing remote patient monitoring, the integration of wireless networks, IoT, and AI is witnessed across diverse domains. This paper navigates through real-world use cases that showcase how this convergence enhances efficiency, accuracy, and resource management. However, with these benefits come challenges. Data privacy, security, and ethical considerations are magnified in this interconnected landscape. Scalability and interoperability pose technical hurdles, necessitating collaborative efforts to ensure responsible and sustainable development. As this convergence advances, emerging trends like edge computing, AI-enabled autonomy, and quantum computing chart the course for the future. These innovations herald a world where seamless connectivity, intelligent decision-making, and exponential computation reshape our understanding of what's possible. In conclusion, the intersection of wireless networks, IoT, and AI transcends mere technological integration. It represents a paradigm shift that bridges the virtual and physical worlds, fostering a connected ecosystem with immense promise and a host of responsibilities. Through exploration, innovation, and ethical considerations, we are poised to harness the full potential of this convergence, shaping a future that is both technologically advanced and ethically aligned.

Artificial intelligence (AI) has emerged as a promising technology that can revolutionize surgical decision-making (SDM). This comprehensive review aims to explore the current state of AI in SDM and highlight its benefits, challenges, and future directions. The integration of AI in SDM offers numerous advantages. AI algorithms can analyze medical images, such as radiographs, computed tomography scans, and magnetic resonance imaging, to detect abnormalities and assist in pre-operative assessments. By leveraging electronic health records, AI can provide personalized surgical recommendations based on patient-specific data.Additionally, AI can analyze genetic data to assess genetic predispositions and tailor treatment plans accordingly. Intra-operatively, AI can aid in real-time analysis of surgical videos and imaging, helping surgeons identify critical structures and guide precise incisions. AI algorithms can also monitor physiological indicators to detect early signs of complications and predict outcomes, improving intra-operative decision-making. Post-operatively, AI can analyze vital signs, imaging, and patient data to detect complications, provide outcomes analysis, and facilitate personalized patient care. However, challenges and limitations exist. Data quality and availability, interpretability of AI algorithms, data security, integration into surgical workflows, and regulatory considerations are important challenges. Addressing these challenges involves ensuring data privacy, developing transparent AI models, establishing robust infrastructure, engaging clinicians, and establishing regulatory frameworks. AI-powered surgical robots and systems can enhance surgical precision and automation. Improvements in interpretability and explainability foster trust and ethical considerations. Also, data sharing and collaboration advancements could refine AI algorithms' accuracy and generalizability. Personalized medicine and precision surgery are achieved through AI integration. Also, education and training could benefit from AI-powered decision support systems.

Edge computing is rapidly becoming a pivotal element in the evolution of consumer electronics, particularly with the increasing use of System-on-Chip (SoC) architectures embedded with artificial intelligence (AI) capabilities. By enabling on-device processing, AI-powered SoCs allow consumer devices to process and analyze data locally, without relying on cloud infrastructure. This whitepaper explores the transformative role of AI in enabling edge computing, focusing on AI-embedded SoCs and their ability to deliver real-time data processing, autonomous decision-making, and improved privacy. The paper highlights key themes such as latency-sensitive applications, local machine learning inference, and the advancements in AI algorithms that drive the success of edge AI in consumer devices like smartphones, wearables, and Internet of Things (IoT) systems. Furthermore, it examines the privacy benefits of edge computing, where data can be processed without ever leaving the device, thus ensuring enhanced data security. Keywords AI-enabled SoCs, edge computing, real-time data processing, IoT devices, local machine learning inference, autonomous decision-making, latency-sensitive applications, privacy improvements, on-device AI, AI in consumer electronics

e-commerce is one such industry that preferred to stay back from the use of AI applications in the initial stages but finally, it has begun its journey towards the recent AI era. E-commerce also has faced a huge paradigm shift using AI-especially machine intelligence (MI), data analytics, and business intelligence. Online businesses nowadays are facing highly notable competition, and data is one of the most valuable resources for the flourishing and nourishing of any business. Presently, most of the businesses are app-based, and they share a virtual marketplace. Therefore, getting insights from the data and generating knowledge and wisdom from the information is the next main aim for getting a better customer experience, better decision-making, recommender system, or setting the dynamic pricing intelligently according to customers’ buying patterns and characteristics. This proposed chapter mainly focuses on the area where different AI techniques are used to improve customer experience in e-commerce. For better customer experience and getting a competitive advantage to create a new marketplace, e-commerce industries are now utilizing data analytics and MI, which in turn, a part of artificial intelligence (AI). Though the perception of AI is not new, widespread application of AI is emerging since the last couple of years, and this new era is the era of AI. 30The proposed chapter says about AI and its types, different AI techniques that are being applied by several e-commerce industries, and the present trends along with the future direction of AI for the e-commerce industry.

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

An adaptive access control scheme based on trust degrees for edge computing

This in-depth research explores the emerging relationship between artificial intelligence (AI) and legal systems by addressing key questions and understanding the evolution of global justice systems. This study focuses on the role of AI in strengthening the efficiency and objectivity of the judiciary, especially through the application of AI as judges in countries such as China and Estonia. This research aims to systematically analyse these developments, examining how AI is being integrated into justice systems in different parts of the world with challenges related to ethics, accountability, and human rights. The study results show that the integration of AI in the legal system brings increased efficiency and potential for transparency but also raises serious concerns about bias in AI algorithms, limitations in interpreting complex laws, and the impact on human rights principles. The main findings of this research show that the integration of AI in the legal system contains great potential for transformation but also requires a careful approach. While AI can improve the efficiency and quality of decision-making, it is important that AI is developed and implemented within a solid legal and ethical framework that respects human rights and maintains the justice system's integrity. This research emphasizes the need to consider each country's unique legal, cultural, and social context when adopting AI into their legal systems.

The integration of artificial intelligence (AI) and image processing techniques has emerged as a transformative solution to address the limitations of traditional cotton fiber quality assessment methods, particularly the High-Volume Instrument (HVI) and Advanced Fiber Information System (AFIS), which require time-consuming manual labor. This comprehensive review examines the convergence of three key technological domains: image processing, AI/machine learning, and IoT/edge computing, in revolutionizing cotton fiber quality assessment. The review focuses on three primary image processing techniques—feature extraction, segmentation, and classification—that enable precise analysis of critical fiber properties including length, fineness, strength, and maturity. Advanced AI algorithms, particularly convolutional neural networks (CNNs), have demonstrated remarkable success in automating the assessment process, achieving accuracy rates of 82-98% in fiber classification tasks. The integration of Internet of Things (IoT) devices and edge computing has further enhanced the system's capabilities, enabled real-time quality assessments and reduced processing time by up to 60% compared to traditional methods. However, several significant challenges persist, including limited availability of high-quality annotated datasets, variability in image quality due to environmental factors, model generalization across different cotton varieties, and real-time processing constraints in industrial settings. The combination of image data with additional sensor inputs, such as spectral analysis and environmental monitoring, offers potential to further enhance assessment accuracy and robustness. This review emphasizes the transformative potential of AI-driven image processing systems in revolutionizing cotton fiber quality assessment, while also identifying critical areas requiring further research for successful industrial implementation. The findings suggest that continued advancements in AI algorithms, coupled with improved IoT integration and edge computing capabilities, will be crucial for developing more robust and efficient quality assessment systems in the cotton industry.

Background: In today's digital age, consumers interact with brands through various channels, including websites, social media, and mobile apps. This has led to the rise of "digital consumers" with distinct cognitive skills and expectations. Businesses are constantly seeking ways to improve customer experience (CX) and stay competitive. Objective: This article explores how artificial intelligence (AI) is transforming the digital consumer experience. Methods: A qualitative research approach was employed, utilizing literature reviews, expert interviews, and case studies to gain insights into the application of AI in customer interactions and its impact on the digital customer journey. Findings: The research found that the integration of AI in digital consumerism has created new trends in business, enabling personalized and dynamic interactions between consumers and brands. AI-powered tools like recommendation systems, chatbots, and virtual assistants blur the lines between pre-purchase, purchase, and post-purchase stages, offering a more seamless customer experience. Conclusions: The integration of artificial intelligence and digital customer experience has the potential to transform the future of e-commerce, marketing, and customer service, opening new horizons for both businesses and consumers. However, challenges related to data privacy, ethics, and algorithmic biases need to be addressed. Originality/Significance: This article presents an original contribution to the understanding of the evolving digital consumer experience and the impact of AI on this landscape. While the use of AI in various aspects of business has been explored