Journal of Artificial Intelligence & Cloud Computing
Journal of Artificial Intelligence & Cloud Computing is an open access peer-reviewed journal from the publishers of Scientific Research and Community (SRC) which publishes articles in the form of written, video and power-point presentations from the arena of Artificial Intelligence & Cloud Computing. The journal focuses to build up accessing the complete content of articles freely from online for reader’s perusal. Articles can be submitted through online from https://www.onlinescientificsresearch.com/submit-online or can send through an e-mail with attachment for artificialintelligence@onlinescientificresearch.net or jaicc@onlinescientificresearch.info
A Sentiment Analysis of Customer Product Review Based on Machine Learning Techniques in E-Commerce
The use of customer evaluations as a basis for purchasing decisions has grown in importance with the meteoric rise of e-commerce in the last several years. Reviews provide businesses with valuable information and foster trust, in addition to helping potential customers make informed decisions. A comprehensive examination of a dataset including reviews from Amazon that spans many product categories was part of this research. This SA project's primary goal is to categorise Amazon reviews as neutral, negative, or positive using a BERT model. A study involves several key stages, including data preprocessing, collection, splitting, model training, and evaluation. The JSON-formatted reviews of items sold by Amazon were compiled, including devices like mobile phones, laptops, televisions, tablets, and video security systems. Preprocessing steps included lowercasing, removal of stop words, punctuation, contractions, tokenization, and part-of-speech tagging. Sentiment scores were generated using an opinion lexicon, and word embeddings were applied for numerical vectorization. Employing a cross-entropy loss function trained inside the PyTorch framework, a BERT model was used for sentiment categorisation. To measure how well the model performed, we employed evaluation criteria like recall, F1-score, precision, and accuracy.BERT achieved superior results compared to logistic regression and decision tree models, demonstrating its ability to capture long-term dependencies in textual data. Empowering e-commerce platforms to make educated judgements and expand their service offerings, these results have substantial practical consequences and will give them confidence in their strategy.
Copyright (c) 2023 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
Mitigating Bias in AI Models through Ethics and Transparency
Artificial Intelligence (AI) systems have become ubiquitous, transforming various sectors such as healthcare, finance, and criminal justice. However, the potential for bias in these systems raises ethical and practical concerns. This paper delves into the sources of bias in AI, including data bias, algorithmic bias, and human bias. Real-world examples illustrate the impacts of biased AI, such as discriminatory lending practices, misdiagnoses in healthcare, and wrongful convictions in criminal justice. The ethical implications of biased AI are explored, emphasizing the need for fairness, equity, and transparency.Mitigation strategies are discussed, focusing on techniques like data cleansing, algorithmic auditing, and ethical AI design principles. Additionally, the importance of regulation and policy frameworks to address bias in AI is highlighted. By promoting ethical and transparent AI practices, this paper aims to contribute to the development of fairer and more responsible AI systems.
A Comprehensive Automated Generation of Functional Coverage and Structured High Verification Plan for Complex Architecture ofGPUs and AI Accelerator
Functional coverage is the most important metric in design verification and writing the functional coverage accurately needs skills and is manual process.Moreover, hitting different complex cross functional cover points requires directed complex testcase. Deploying AI to write the cover points and writing sequences to hit complex scenarios allows easier test writing and converging on functional coverage for complex architectures such as graphics-purpose unit GPU and AI accelerators. Script also allows to create a high verification plan and analyze effectively. This HVP plan can also be used across DUTs that allows to measure effective coverage across different levels of verifications abstractions.
Harnessing Apache Airflow Operators for Enhanced Workflow Automation
This paper explores the utilization of Apache Airflow operators to enhance workflow automation, specifically focusing on the Python, MySQL, Oracle,Oracle Stored Procedure, Bash, and Rabbit MQ operators. Using these operators, companies can standardize and automate their data workflows, resulting in increased productivity and performance. The post emphasizes the implementation, advantages, and future possibilities to work with these operators in Apache Airflow. The paper concludes with an examination of how Apache Airflow operators can be employed as part of workflow automation, alongside the breadth they span and their adoption for the future.
The Human Element in AI: Balancing Automation with Empathy in E-Commerce
Artificial Intelligence (AI) has become an integral part of e-commerce, automating various aspects from customer service to personalized recommendations.While AI significantly enhances efficiency and personalization, there is an emerging need to balance automation with the human touch to maintain empathy and emotional connection with customers. This paper explores the integration of human elements in AI-driven e-commerce platforms, focusing on achieving a balance between automation and empathy. The findings emphasize the importance of empathy in customer interactions and suggest strategies for incorporating human elements into AI systems to enhance customer satisfaction and loyalty.
Transforming Travel Planning: The Impact of Generative AI on Itinerary Optimization, Cost Efficiency and User Experience
The travel industry has been undergoing a revolution for a few years now due to rapid technological advancements. This article assesses the application of Generative AI in travel planning, focusing on itinerary generation, low-fare finding, no-crowd planning, and route optimization. The study employed a quantitative methodology with 10 participants using an AI-powered website. Structured surveys gathered feedback on usability, efficiency, and satisfaction. Users were very satisfied with the AI-enhanced website, especially in terms of navigation, personalized itineraries, price accuracy, and data security. However, areas for improvement in time efficiency were indicated. The study concludes that Generative AI significantly improves trip planning,offering convenience, personalization, and efficiency. Future work should focus on refining AI features and exploring additional applications to further optimize travel planning.
Advancements in Natural Language Processing (NLP) and Its Applications in Voice Assistants and Chatbots
Natural Language Processing (NLP) has witnessed remarkable advancements in recent years, revolutionizing various fields including voice assistants and chatbots. This paper explores the latest developments in NLP technologies and their applications in enhancing the functionality and intelligence of voice assistants and chatbots. Beginning with an overview of NLP and its core components, such as tokenization, syntactic analysis, semantic understanding,and machine learning techniques, this paper delves into the cutting-edge techniques driving the evolution of NLP. Furthermore, it discusses the challenges faced by NLP systems and the strategies employed to address them. Subsequently, the paper explores the utilization of NLP in voice assistants and chatbots,elucidating how these technologies are reshaping human-computer interaction, improving user experiences, and expanding the scope of applications in various domains. Additionally, it examines the impact of NLP advancements on the future trajectory of voice assistants and chatbots, highlighting potential opportunities and implications for research and industry.
Unlocking Integration Efficiency with Entire-X
EntireX stands as a robust middleware solution, pivotal in facilitating seamless integration and communication between disparate systems and platforms within an enterprise architecture. This abstract delves into the core principles and functionalities of EntireX, elucidating its significance in optimizing integration processes and enhancing operational efficiency. At its core, EntireX serves as a comprehensive middleware platform, offering a suite of tools and services designed to streamline the integration of diverse applications, databases, and technologies. Leveraging messaging protocols such as Remote Procedure Call (RPC) and Message Queuing Telemetry Transport (MQTT), EntireX enables real-time, asynchronous communication between heterogeneous systems, irrespective of their underlying technologies or architectures. Key features of EntireX include its robust support for various programming languages, including Natural, COBOL, Java, and .NET, facilitating seamless interoperability between legacy and modern systems. Additionally, EntireX provides extensive monitoring, management, and administration capabilities,empowering organizations to monitor and optimize integration workflows in real-time. Moreover, EntireX embraces industry standards and protocols, ensuring compatibility with a wide range of technologies and platforms. Its support for secure communication protocols, such as SSL/TLS encryption and digital signatures, enhances data security and confidentiality, safeguarding sensitive information exchanged between systems. Organizations across diverse sectors, including finance, healthcare, manufacturing, and telecommunications, leverage EntireX to address complex integration challenges, such as legacy modernization, data synchronization, and business process automation. By enabling seamless communication and collaboration between systems, EntireX facilitates the efficient exchange of information, accelerates decision-making processes, and enhances overall operational agility. In conclusion, EntireX emerges as a cornerstone in modern integration architectures, offering organizations a comprehensive solution for orchestrating and optimizing communication between disparate systems and technologies. With its robust features, industry-standard compliance, and versatility, EntireX empowers organizations to unlock integration efficiency, drive digital transformation initiatives, and gain a competitive edge in today’s dynamic business landscape.
Integrating IoT with Big Data Platforms: Challenges, Technologies and Strategic Business Insights
The proliferation of Internet of Things (IoT) devices has ushered in an era where vast amounts of data are generated at the edge of network architectures,presenting both unprecedented challenges and opportunities for big data ingestion systems. This paper explores the complexities and strategic advantages associated with the integration of IoT data into big data platforms, highlighting how this convergence is reshaping business analytics and decision-making processes. IoT devices, characterized by their ability to generate continuous streams of real-time data, pose significant challenges in terms of volume, velocity, and variety. The traditional data ingestion models are often inadequate to handle the scale and speed required for effective IoT integration. This necessitates the adoption of advanced data processing frameworks and architectures that can not only manage the high throughput of data but also accommodate the heterogeneous nature of IoT-generated information. This analysis delves into several key areas: the technical challenges of IoT data management, including issues related to data volume, velocity, and variety; the role of modern data ingestion technologies such as Apache Kafka and Apache Storm, which facilitate real-time data processing; and the integration of cloud platforms like AWS IoT Core, Azure IoT Hub, and Google IoT Core that enhance the scalability and efficiency of data ingestion operations. Moreover, the paper discusses the significant opportunities that arise from effective IoT and big data integration, such as enhanced real-time decision-making and predictive analytics, which can lead to improved operational efficiencies and competitive advantages in various industries including manufacturing,healthcare, and urban development. In conclusion, while the integration of IoT with big data ingestion presents considerable challenges, it also offers substantial benefits for organizations looking to leverage deeper insights into their operations and markets. The paper provides actionable recommendations for businesses to navigate this complex landscape, ensuring they can capitalize on the full potential of IoT-driven data analytics.
Investigating the Role of Exploratory Testing in Agile Software Development: A Case Study Analysis
This abstract provides a thorough examination of the critical function of exploratory testing in the framework of Agile software development approaches,drawing conclusions from a careful examination of case studies. Agile approaches have become a top framework for encouraging adaptability, cooperation,and continuous improvement in response to the software development processes' ever-increasing complexity and quick evolution. In such a dynamic environment, exploratory testing is a vital tool that helps teams discover bugs, iteratively explore software features, and improve testing methods. This paper clarifies the complex interactions between exploratory testing and Agile principles, illuminating their effects on project outcomes, team dynamics, and product quality through a thorough analysis of several case studies taken from various industry contexts. This study offers useful insights and suggestions for improving testing procedures and streamlining software development processes by combining empirical data and theoretical frameworks to better understand how exploratory testing can be successfully integrated into Agile environments. Finally, this study emphasises the importance of exploratory testing as a fundamental component of Agile approaches and argues for the deliberate application of this practice to promote innovation, flexibility, and quality control in contemporary software development projects.
Copyright (c) 2025 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
Brain-Computer Interface, Generative AI (GAI)
Brain-computer interface (BCI) is an interface (or sort of a system) by which humans can communicate with computers. The real question arises about why humans must communicate with the computer via brain signals. There are over a million or more people suffering from diseases or malfunctions of motor ability where mobility is limited, or there is no mobile at all. This interface allows these individuals to communicate with the outside world. Such diseases would rise yearly as more techniques and methods become available to diagnose them. While there is no cure for such injuries or diseases, there may be a stopgap to fill the communication void. We take communication for granted, whereas a person with limited mobility is a boon.
Since the pandemic, a tremendous amount of research has been conducted on the bio signals and detection of any symptoms early on. Catching symptoms early using the bio-signals would enable early detection, preventive care in some cases, and the potential to create a cure plan for the individual. There are many ways in which the bio-signals can be detected and processed meaningfully. These data would help the individual and be used to train the biosignal-based models. Having these models generate large amounts of data trained on this dataset would enable the detection, diagnosis, and preventive cure more efficiently than ever.
Applying Machine Learning Techniques to Evaluate Climate-Related Risks in Real Estate Mortgage Valuations
Facing the escalating effects of climate change, the real estate industry faces risks from physical perils and shifting towards a low-carbon economic model.These risks have substantial consequences for the assessment and effectiveness of real estate mortgage portfolios. Conventional approaches to evaluating mortgage risk frequently fail to capture the intricate, non-linear connections between climate variables and loan results. In this paper, we present a new machine-learning framework that aims to quantify climate-related risks in real estate finance. We utilize neural networks and gradient-boosting algorithms to forecast the likelihood of mortgage defaults and the potential loss resulting from defaults in different climate stress scenarios. A robust and forward looking risk assessment is developed by integrating property-level exposure data, loan characteristics, and macroeconomic indicators. The empirical findings prove that our models perform superior to conventional econometric methods regarding predictive precision and computational effectiveness.The framework offers a robust instrument for investors, lenders, and regulators who aim to effectively address climate risks and enhance their ability to withstand and adapt to an unpredictable future.
Leveraging Data Analytics for Productivity and Compliance in Banking
In the modern banking world, data analytics have become a significant factor in making operational processes more efficient, more accurate in risk management, and more compliant with regulations. This paper examines the various aspects of data-driven banking decision-making and its role in productivity improvements and compliance. Data analytics solutions, which address specific challenges banks are experiencing, are further explained in this research, ultimately revealing the disruptive power of stepping up one's data activities. The results underline the importance of data analytics as a tool that may be used to improve or is required to process and detect risks and standards closely.
Investigation of Firmware Securing IoT Devices at the Chip Level Using AI and ML Approaches: A Concise Systematic LiteratureReview
System-on-Chip (SoC) designs are becoming increasingly intricate as they integrate various components and functionalities onto a single chip. This intricacy brings forth new vulnerabilities and attack surfaces, presenting a challenging task to ensure robust cybersecurity. Although previous studies have explored different AI approaches, there is a need to delve into the complexities of modern SoCs and propose practical methods to secure these systems,thereby aiding researchers in mitigating potential threats. The primary objective of this paper is to uncover the opportunities that emerge from utilizing AI techniques in SoC designs to fortify cybersecurity defenses. Subsequently, the study aims to deliberate on the complexities involved in integrating AI with SoC and provide appropriate recommendations to address these intricacies. This review paper will identify and discuss the technical, architectural,and implementation challenges faced by researchers and practitioners when incorporating AI into SoC designs for cybersecurity purposes. Moreover, it will suggest with potential solutions and strategies to effectively tackle these challenges. Furthermore, it aims to aid future researchers by offering potential research directions to enhance security in AI-SoC through innovative solutions. By identifying gaps in current literature and highlighting areas that require further exploration, this paper will guide future researchers in developing advanced and secure AI-SoC systems for cybersecurity applications. Overall, this review paper will contribute to the existing body of knowledge by providing a comprehensive analysis of the impact that integrating AI and SoC has on cybersecurity and foster advancements in AI-SoC systems that effectively enhance cybersecurity measures.
Enhancing Digital User Experiences through Personalized Interfaces and Predictive Modelling
This paper explores the development and implementation of a personalization strategy designed to enhance user experiences on digital platforms. By leveraging user behaviour data and machine learning algorithms, we propose a methodology for creating adaptive user interfaces, predictive actions, and personalized user journeys. The paper presents a real-time example application, complete with data analysis, user segmentation, feature engineering, predictive modelling, and a comprehensive A/B testing framework. We aim to demonstrate how personalization can significantly improve user engagement, satisfaction, and platform efficacy.
The Role of Data Catalogs in Driving Organizational Insights and Efficiency
In 2023, data catalogs have become indispensable in data management, facilitating efficient data discovery and enhancing metadata management. As centralized metadata repositories, they empower organizations to navigate their vast data landscapes efficiently, thereby improving decision-making and data governance. This paper delves into the role of data catalogs within modern data management practices, emphasizing their contribution to organizational efficiency and insight generation. Data catalogs play a pivotal role in modern data management by facilitating efficient data discovery and streamlining metadata management processes. These catalogs serve as centralized repositories of metadata, enabling users to easily search, explore, and understand available data assets within an organization. By providing comprehensive metadata descriptions and search capabilities, data catalogs empower organizations to maximize the value of their data, improve decision-making processes, and enhance overall data governance. This paper explores the importance of data catalogs in modern data environments, highlighting their role in enhancing data discovery and metadata management for improved organizational efficiency.
Harnessing AI for Advanced Cybersecurity in System on Chip Design a Compendious Study
Optimizing User Engagement in Enterprise Knowledge Management:Insights and Innovations from a Confluence Pages Project
In the pursuit of enhancing user engagement within Confluence-based knowledge repositories, this paper details the development of an advanced scoring system tailored to evaluate content efficacy. Central to this system is the normalization of disparate content metrics against a common scale and the strategic weighting of key performance indicators—such as engagement, timeliness of updates, frequency of edits, content length, and collaborative contributions.The confluence of these metrics culminates in a composite score per content piece, enabling a nuanced appraisal of its value. Employing Z-scores for benchmarking facilitates the delineation of content into actionable tiers, thus informing a data-driven approach to content management. This scoring framework, with its capacity to quantify and qualify user-content interaction, presents a transformative tool for knowledge management, ensuring that content not only resonates with its audience but also aligns with organizational objectives.
The AI-powered PM Toolkit - A Project Manager’s Guide to Thrive the Generative AI Wave
Project management stands at the precipice of a significant transformation driven by Generative AI (GenAI). This paper delves into the profound impact of GenAI on project managers, exploring how it reshapes roles, responsibilities, and the future of the field. We begin by outlining the evolution of AI,highlighting the emergence of GenAI and its ability to go beyond analysis and create novel content. GenAI will transform project management responsibilities from fully automated activities to a blend of assisted and augmented tasks, requiring collaboration and strategic application of this innovative technology. The paper explores the evolution of project management methodologies and the evolving skill set required of successful project managers in the age of AI and offers a "Project Manager's Guide to Thriving in the Generative AI Tsunami" by outlining the three pillars of the PMI Talent Triangle: Ways of Working,Power Skills, and Business Acumen. Each pillar is examined in detail, highlighting the role of GenAI in enhancing mastery and providing illustrative examples of AI-powered tools and frameworks. This paper equips project managers with the knowledge and tools to not only survive but thrive in the age of GenAI, ensuring their continued value and leadership in shaping successful projects.
Machine Learning for Predictive Observability - A Study Paper
Since artificial intelligence and machine learning have taken off over the last six years, predictive observability has progressed fast. This paper addresses the progress in machine learning technologies of leveraging observability data for anomaly detection, forecasting, and reliability prediction from 2016 to 2022. It centers on how machine learning developments have championed more proactive observation, distinguishing these from reactive observability.The review discusses unsupervised learning methods for anomaly detection, including isolation forests and autoencoders. The idea is to detect suspicious entities early when they have yet to cause harm to users. Likewise, LSTMs have shown effectiveness for forecasting critical time series of vital metrics to predict the capacity and performance issues to avoid them. Failure modeling methods like survival analysis secure the risk of failure and provide reliability improvement indicators. The transformers and the adversarial machine learning approaches are listed as the breakthroughs that lead to enhanced predictivity on noisy data. Accurate metrics quantified are 60-70% better tip-off early in the incident, a 25-50% decrease in user-affecting failures, and up to 30% shorter mean time to recovery. Companies that have already deployed probabilistic observability on a large scale, like Google, Stripe, IBM, and Alibaba, are reviewed. This summary concludes that in the past six years, applied machine learning has grown exponentially for predictive observability, giving teams proactive and preventive management capability. Advancement will occur in the future, along with the ability to harness new ML methods and deployment in real-world applications, which will see an impact on the field.
Challenges and Solutions in Cross-Platform ML Systems Integration
This paper explores the complexities of cross-platform integration within the realms of Data Engineering and Machine Learning Operations (MLOps),crucial for enhancing the lifecycle management of machine learning (ML) models. It delves into the challenges and strategies associated with integrating diverse technologies and practices to improve the scalability, reliability, and maintainability of ML systems. The paper highlights the significance of addressing technical, organizational, and cultural barriers to foster a cohesive, agile environment conducive to the iterative development and operationalization of ML models. Through a detailed examination of the multifaceted nature of these challenges, including technical interoperability, organizational alignment, and fostering a collaborative culture, the paper provides insights into navigating the complexities of integrating disparate technologies and methodologies. It also underscores the importance of strategic organizational changes, technological innovation, and a commitment to data governance and collaborative culture in achieving successful cross-platform integration. A case study of a financial firm’s integration of ML within its call center operations exemplifies the transformative potential and practical implications of effectively addressing these challenges. The paper concludes by arguing that the seamless integration of platforms and tools extends beyond technical endeavors to encompass strategic, organizational, and cultural shifts, illuminating the path toward optimizing the efficiency and effectiveness of ML model lifecycles through MLOps.
Innovative Ways of Utilizing Cloud Computing with a Backendless Database and Other Optimization Methods
Cloud computing is a revolutionary concept that has changed the way that organizations manage and process data. The cloud has provided companies with new opportunities for scalability, cost-efficiency, and flexibility in their operations. This article explores innovative ways of utilizing cloud computing, specifically with a backendless database, and various optimization methods to maximize the benefits of this technology. It also discusses the advantages of backendless databases, integration with cloud platforms, optimization techniques, and provides real-world examples of successful implementations. It serves to demonstrate how organizations can harness the power of cloud computing and backendless databases for improved performance and competitiveness.
Data Privacy and Risk Management, Collaboration is Key on Tackling Privacy Risks/Issues
Data Privacy is relevant for majority of the firms conducting businesses in the international market today. Better privacy safeguards and implementations are one of the key contributors in driving business output, companies are reporting direct and indirect benefits as a result of privacy project related investments.To be compliant with the Privacy regulation landscape globally is the need of the hour, there have been significant business impacts in terms of both financial and reputational, as a result of noncompliance to privacy regulations. To manage these risks, it is imperative for the Privacy assessment teams, Legal teams and Risk management teams to work together and tackle these challenges. This paper firstly aims at giving a perspective on global risk landscape and how the privacy regulations are gaining more traction every passing day, bringing in more states and countries into the fold. This article also narrates the approach or solution explaining how Risk management teams can partner with the relevant stakeholders in legal and privacy teams and guide the data privacy risks across firms, across business sectors. Through the risk management lifecycle thus remediating these risks, helping businesses prosper and at the same time safeguard individual interests.
PII (Personally Identifiable Information)
The Role of Machine Learning in Identifying Risk Factors for Adverse Events in Healthcare: A Comprehensive Overview
This paper presents a comprehensive exploration of the use of machine learning (ML) in identifying risk factors for adverse events in healthcare. It delves into the challenges and opportunities associated with ML techniques, emphasizing the potential impact of ML-based risk factor identification on clinical decision-making. The paper also discusses the need for interdisciplinary collaboration and continuous innovation to maximize the potential of ML in enhancing patient safety and healthcare quality. By examining various ML techniques, challenges in utilization, opportunities and impact, interdisciplinary collaboration, and future prospects, this paper provides valuable insights for researchers, healthcare professionals, and policymakers.
Impact of Building Automation on Indoor Air Quality and HVAC Performance
In response to the growing emphasis on healthier indoor environments and sustainable building practices, this research delves into the intricate relationship between building automation and key facets of indoor environmental quality. Specifically, the study focuses on the impact of building automation systems on Indoor Air Quality (IAQ) and the overall performance of Heating, Ventilation, and Air Conditioning (HVAC) systems. With the increasing demand for enhanced IAQ, building automation emerges as a crucial tool for real-time monitoring and optimization of various IAQ parameters. The research explores the comprehensive integration of automation technologies within building systems, elucidating their multifaceted effects on IAQ. By scrutinizing the intricate interplay between automation systems and HVAC components, the study unveils the ways in which automation technology influences and contributes to IAQ management. The investigation encompasses a thorough examination of sensor technologies, control algorithms, and data analytics employed in building automation to ensure a nuanced understanding of their impact on IAQ. Furthermore, the study elucidates the broader implications of building automation on HVAC system performance. It investigates how automated control mechanisms, coupled with real-time data analytics, contribute to the efficient operation of HVAC systems in maintaining optimal IAQ. The research contemplates the synergies between automation technologies and HVAC components, emphasizing their role in not only ensuring healthier indoor environments but also in achieving energy-efficient and sustainable HVAC operation.
Application of Generative AI for Business Analyst role
This paper presents a comprehensive study on the application of Generative AI, specifically focusing on its role in enhancing the business analyst role within the realm of software development. It delves into the nuances of requirements engineering, a crucial phase in the software development lifecycle, and the transformation of user requirements into user stories. The paper explores the use of natural languages for crafting user requirements and emphasizes the revolutionary impact of Generative AI, particularly Open AI's Chat GPT, in automating and refining the process of generating user stories, acceptance criteria, and the definition of done. It speaks about the prompt-compression method with “LLM Lingua,” and conversation chaining using “Lang chain” aimed at optimizing the efficiency of large language models in handling extensive requirements, thereby signifying a significant stride in the field of software development and requirements engineering.
Exploring Real-World Applications of GenAI in Retail
In this paper we provided a perspective on the integration of Artificial Intelligence (AI) in the retail sector, highlighting their transformative potential in reshaping industry dynamics. It delves into the multifaceted applications of AI in retail, including inventory management, personalized shopping experiences, dynamic outreach, and conversational support, underscoring how these technologies drive customer engagement, operational efficiency, and innovation in product and service offerings. Emphasizing the necessity of adopting these technologies, the paper contends that AI is not mere competitive advantages but crucial for maintaining relevance in the swiftly evolving retail landscape. The strategic integration of these technologies presents significant opportunities for growth and differentiation, although it necessitates substantial investment in resources and workforce upskilling.
Utilizing Gen AI and Computer Vision for Applications in the Retail Sector
In this paper we provided a perspective on the integration of Artificial Intelligence (AI) and Computer Vision in the retail sector, highlighting their transformative potential in reshaping industry dynamics. It delves into the multifaceted applications of AI in retail, including inventory management,personalized shopping experiences, dynamic outreach, and conversational support, underscoring how these technologies drive customer engagement,operational efficiency, and innovation in product and service offerings.Moreover, the study explores the role of Computer Vision in revolutionizing retail experiences through self-checkout systems, inventory management, store layout optimization, and AI-based loss prevention. Emphasizing the necessity of adopting these technologies, the paper contends that AI and Computer Vision are not mere competitive advantages but crucial for maintaining relevance in the swiftly evolving retail landscape. The strategic integration of these technologies presents significant opportunities for growth and differentiation,although it necessitates substantial investment in resources and workforce upskilling.
AI in Cybersecurity and User Interface Design beyond Chatbots
This paper examines the integration of Artificial Intelligence (AI) in cybersecurity, highlighting how AI is revolutionizing cloud security for enterprises amidst increasing cyber threats. It defines key concepts including AI, Machine Learning, Large Language Models, Natural Language Processing, and Generative AI, and explores their applications in cybersecurity. Focused on enhancing the efficiency and efficacy of cybersecurity solutions, the paper delves into AI-driven technologies like Text-to-Visualization, Breach Analytics, and Multi-modal Data Protection. It also discusses the impact of AI on user experience (UX) design, advocating for user-centric, intuitive interfaces in cybersecurity tools. The paper concludes by emphasizing the importance of ethical considerations in AI deployment and the crucial role of UX designers in shaping the future of AI-enhanced cybersecurity solutions.delves into AI-driven technologies like Text-to-Visualization, Breach Analytics, and Multi-modal Data Protection. It also discusses the impact of AI on user experience (UX) design, advocating for user-centric, intuitive interfaces in cybersecurity tools. The paper concludes by emphasizing the importance of ethical considerations in AI deployment and the crucial role of UX designers in shaping the future of AI-enhanced cybersecurity solutions.