Intelligent Solutions Research Articles

A Framework for Real-Time AI-Driven Secure Code Analysis Integrated with DevSecOps in Cloud-Native CI/CD Pipelines

Abstract—This paper presents a novel framework for inte- grating real-time AI-driven secure code analysis into DevSecOps practices within cloud-native CI/CD pipelines. As organizations increasingly adopt cloud- native architectures and agile develop- ment methodologies, the need for robust, automated security mea- sures becomes paramount. Our proposed framework leverages advanced machine learning algorithms to perform continuous, real-time code analysis, identifying potential vulnerabilities and security risks throughout the development lifecycle. By seamlessly integrating with existing CI/CD tools and cloud platforms, our solution enables organizations to enforce security policies, detect threats, and remediate issues without compromising development velocity. We evaluate the effectiveness of our framework through a series of case studies across diverse software projects, demon- strating significant improvements in threat detection accuracy, reduced false positives, and overall security posture. Our results indicate that the proposed AI-driven approach can enhance code security by up to 40% compared to traditional static analysis tools, while maintaining the agility of modern development prac- tices. This research contributes to the evolving field of DevSecOps by offering a scalable, intelligent solution for embedding security into the heart of cloud-native software development processes. Index Terms—DevSecOps, AI-driven security, cloud- native, CI/CD pipelines, secure code analysis

Smart Caption: Intelligent Image Description Using Transformer Decoder

The need for automated image description systems has grown significantly with the rise of multimedia content across diverse domains such as social media, digital libraries, and accessibility technologies. Smart Caption presents a novel approach for generating intelligent and context-aware image descriptions by utilizing a combination of Vision Transformers (ViTs) and Natural Language Processing (NLP) Transformer decoders. Unlike traditional convolution-based methods, Vision Transformers treat images as sequences of patches, enabling them to capture global image features more effectively. These extracted visual features are then processed by a Transformer-based decoder to produce coherent and contextually appropriate captions, bridging the gap between image recognition and natural language generation. Our approach focuses on leveraging the attention mechanisms inherent in both Vision and NLP Transformers to enhance the quality of image descriptions. The ViT architecture is designed to focus on relevant regions within an image, while the NLP Transformer decoder is adept at generating fluent and detailed descriptions by attending to the most significant visual features. This two-stage process improves the precision of object detection, relationship understanding, and scene context in generated captions. Experimental results demonstrate that Smart Caption out performs traditional methods in terms of accuracy, contextual relevance, and fluency, marking a significant step forward in the field of automated image captioning. Through this research, we aim to provide a scalable, efficient, and intelligent solution for image description, with potential applications in areas such as accessibility tools for visually impaired users, digital content indexing, and automated content generation. Our findings highlight the strengths of transformer-based models in bridging vision and language tasks, offering promising directions for future research in multimodal AI..

Bibliometric analysis on the factors that affect the cycle of formulation, evaluation, and management of social project

Background Projects enable the resolution of problems and alter societal realities, assuming they represent the optimal choices to modify living conditions positively. If projects are deemed intelligent solutions to problems, why do they fail? This question will be addressed in this article through bibliometric analysis. Methods A bibliometric analysis was conducted on the success or failure factors influencing social investment projects, through a descriptive study with search equations, utilizing a comparative matrix to analyze 32 articles from Scopus and 22 from Web of Science. Results As a result, the number of publications, the themes addressed, and their origins between 2018 and 2023 were identified. The VOSviewer software allowed for identifying keywords and their relationships; with this input, a review of scientific production was conducted to subsequently identify the factors influencing projects and how they have been addressed within their contexts. Conclusions In conclusion, it was found that both the factors influencing success or failure in projects are permeated by the environment and the impact of political, social, and economic factors, in addition to the interaction of elements related to soft and hard skills.

AI-powered precision: breast carcinoma diagnosis through digital proliferation index (Ki-67) assessment in pathological anatomy

PurposeThe primary objective of the study is to enhance the accuracy and efficiency of assessing the proliferation index in cancer cells, specifically focusing on the role of Ki-67. The purpose is to address the limitations of traditional visual assessments conducted by pathologists by integrating AI technologies, particularly deep learning. By accurately computing the percentage of Ki-67-labeled cells, the research aims to streamline the diagnostic process, reduce subjectivity and contribute to the advancement of diagnostic precision in pathological anatomy.Design/methodology/approachThe research employs a methodological approach that integrates Ki-67, a non-histone nuclear protein, as a vital biomarker for assessing the proliferative status of cancer cells. Given the challenges associated with traditional visual assessments by pathologists, including inter- and intra-observer variability and time-consuming efforts, the study adopts a novel methodology leveraging artificial intelligence (AI) solutions. Deep learning is applied to precisely calculate the percentage of Ki-67-labeled cells. The process involves pathologists delineating the tumor area at x40 magnification, enabling the segmentation of various cell types (positive, negative and tumor-infiltrating lymphocytes). The subsequent percentage calculation enhances efficiency and minimizes subjectivity in the diagnostic process.FindingsDespite inherent errors, the research findings indicate that the model surpasses existing benchmarks, showcasing superior accuracy in terms of average error measurement. The comparison with diverse datasets and benchmarking against pathologists’ diagnoses contributes empirical evidence to support the effectiveness of the AI-based model in accurately computing the percentage of Ki-67-labeled cells. These findings signify a noteworthy advancement in diagnostic methodologies and reinforce the potential of AI technologies in improving the precision of cancer diagnostics within the realm of pathological anatomy.Originality/valueThe research contributes to the field by introducing an innovative approach that combines Ki-67 as a biomarker and AI technologies for improved diagnostic precision. The originality lies in the utilization of deep learning to calculate the percentage of labeled cells, mitigating the challenges associated with manual assessments. The validation of the model against diverse datasets and benchmarking against pathologists’ diagnoses demonstrates its superior accuracy, highlighting the value of integrating AI in pathological anatomy for enhanced diagnostic outcomes. The study represents a significant stride in original research, offering novel insights and methodologies in the pursuit of more precise cancer diagnostics.

Data-Driven Agile Leadership: The Impact of Business Intelligence Solutions on Transformation Outcomes

This article examines the integration of Business Intelligence (BI) solutions in empowering leadership teams during Agile transformations within organizations. As businesses increasingly adopt Agile methodologies to enhance adaptability and efficiency, leaders face unique challenges in monitoring and steering these complex transformations. Through a multi-case study approach, we investigate how BI tools provide real-time insights into key performance indicators such as sprint velocity and team capacity, significantly enhancing leadership's ability to make informed decisions and identify bottlenecks in the Agile process. Our findings reveal that the implementation of BI solutions fosters a culture of transparency and accountability, enabling leaders to navigate the intricacies of Agile transformation more effectively. The article contributes to the growing body of literature on Agile leadership by highlighting the symbiotic relationship between data-driven decision-making and successful organizational change. We conclude that the strategic use of BI in Agile environments not only improves visibility into transformation progress but also enhances overall organizational performance in response to dynamic market demands.

Human-Centered Robotic System for Agricultural Applications: Design, Development, and Field Evaluation

This paper introduce advancements in agricultural robotics in response to the increasing demand for automation in agriculture. Our research aims to develop humancentered agricultural robotic systems designed to enhance efficiency, sustainability, and user experience across diverse farming environments. We focus on essential applications where human labor and experience significantly impact performance, addressing four primary robotic systems, i.e., harvesting robots, intelligent spraying robots, autonomous driving robots for greenhouse operations, and multirobot systems, as a method to expand functionality and improve performance. Each system is designed to operate in unstructured agricultural environments, adapting to specific needs. The harvesting robots address the laborintensive demands of crop collection, while intelligent spraying robots improve precision in pesticide application. Autonomous driving robots ensure reliable navigation within controlled environments, and multirobot systems enhance operational efficiency through optimized collaboration. Through these contributions, this study offers insights into the future of agricultural robotics, emphasizing the transformative potential of integrated, experience-driven intelligent solutions that complement and support human labor in digital agriculture.

Unsupervised 3D seismic data reconstruction using a weighted-attentive deep-learning framework

The physical and cost limitations of seismic data acquisition often result in the spatial undersampling of data, which has a detrimental impact on subsequent data processing. Seismic data reconstruction plays a crucial role in recovering missing traces arising from the acquisition process. In recent years, deep learning (DL) has emerged as an intelligent solution for this purpose. We introduce an innovative approach called the weighted-attentive DL framework for unsupervised 3D seismic data reconstruction, which combines an attentive transformer network (ATNet) with a conventional projection onto convex sets (POCS) algorithm. Our framework follows the plug-and-play concept, requiring only the original subsampled data to achieve robust performance. It accomplishes this by iteratively updating ATNet parameters, wherein ATNet serves as the reconstruction operator during the DL process. This approach allows us to simultaneously recover missing traces and filter out random noise, with a key feature being its enhanced attention to the observed signal compared with convolutional neural networks. In addition, we incorporate the POCS algorithm into our framework to introduce a linear decay weight for the aliasing space containing noise and signal during the reconstruction process. We rigorously evaluate our method against four existing approaches: adaptive POCS, fast dictionary learning sequential generalized K-means, optimally damped rank-reduction, and DenseNet methods. Our comparative experiments, conducted on synthetic and field data examples, clearly demonstrate the substantial improvements achieved by our method in terms of reconstruction and denoising when compared with the four benchmarked methods.

Long-term investigation of uniaxial compressive and self-sensing performances of ultra-high performance seawater sea-sand concrete incorporating superfine stainless wires: Insights into underlying mechanisms and theoretical models

Developing intrinsic self-sensing concrete is expected to provide a digital intelligence solution to address the infrastructure’s challenges in terms of safety, lifespan, resilience, and carbon emission reduction. Using seawater and sea-sand as well as corrosion-resistant conductive fillers to fabricate self-sensing ultra-high seawater sea-sand performance concrete (UHPSSC) with outstanding mechanical and durability performances is the prerequisite for realizing localized resource utilization and in-situ monitoring of marine infrastructure. However, the potential effect of ions from seawater and sea-sand on the long-term stability of mechanical and electrical performances cannot be ignored. This paper presents a long-term investigation of uniaxial compressive, electrical, and self-sensing performances of superfine stainless wires (SWs) reinforced UHPSSC under natural curing. The results show that incorporating 1.5% SWs improves the compressive strength, elastic modulus, peak strain, and toughness of UHPSSC by 25.0%, 11.7%, 33.8%, and 119.2%, respectively. The established statistical damage constitutive models based on Weibull strength theory highlight the roles of SWs in delaying crack initiation. The dense microstructure of SWs-reinforced UHPSSC inhibits the growth of expansive hydration products formed by corrosive ions, thus guaranteeing its long-term strength development. Additionally, due to incorporating 1.5% SWs, the electrical resistivity of UHPSSC is reduced by seven orders of magnitude, with a strain sensitivity of 119.1 under monotonic compressive loading and a monitoring stress range of 0-148.4 MPa, indicating that SWs-reinforced UHPSSC can sense its stress and strain while providing early warning of crack initiation and propagation. Benefiting from the rust-free property, micron diameter, and high aspect ratio of SWs, the primary conductive path of composites comprises the overlapping networks formed by low-content SWs, which are not influenced by the ion conduction of conductive ions. Hence, SWs-reinforced UHPSSC exhibits stable electrical resistivity and sensitivity during long-term curing, suggesting its considerable potential for long-term in-situ monitoring of marine infrastructure.

Technology Gap Analysis on the BIM-Enabled Design Process of Prefabricated Buildings: An Autoethnographic Study

This research explores the pivotal role of Building Information Modeling (BIM) technology in revolutionizing the design and construction of prefabricated buildings. It highlights the benefits of BIM-enabled design processes, including improved coordination and efficiency, while addressing challenges in integrating BIM with prefabricated elements. The study proposes artificial intelligence (AI) solutions to enhance architectural detailing, optimize workflows, and facilitate human–robot collaboration. Using autoethnographic methods, the research refines BIM-forward design through thematic analysis and technology gap (TG) assessments. It advocates for a comprehensive technology roadmap that emphasizes BIM libraries, external data sources, and the transition to a multi-dimensional BIM environment. This research contributes valuable insights to advance BIM practices in prefabricated building construction and offers a strategic framework to bridge existing TGs, enabling seamless integration of BIM and AI in future practices.

Enhancing Road Safety with Smart Infrastructure: Development and Implementation of Advanced Zebra Crossings and Speed Breaking

Abstract: This study addresses a critical issue in road safety by proposing innovative solutions to mitigate accidents caused by speeding and reckless driving, particularly in high-risk areas such as highways, hospitals, and schools. With road traffic accidents accounting for approximately 1.3 million fatalities annually, along with 20 to 50 million injuries or disabilities, the inefficacy of traditional traffic control systems is a growing concern. Factors such as poor visibility, adverse weather, and driver distractions further compromise the effectiveness of conventional signs and signals. This research introduces a "smart zebra crossing" system that integrates ultrasonic sensors and embedded LED lights to provide real-time alerts to both drivers and pedestrians, thereby enhancing visibility and safety, especially under low-light conditions. Additionally, a "smart speed breaking" system is proposed, which dynamically adjusts its height based on vehicle speed, detected through ultrasonic sensors, prompting drivers to decelerate when necessary. Both systems are linked to a central traffic management network, facilitating real-time adjustments in speed limits and providing alerts to connected vehicles. The integration of these technologies represents a significant advancement in road safety, offering an adaptive, intelligent solution for improving traffic management and reducing the risk of accidents, particularly in vulnerable zones.

Edge Artificial Intelligence for Electrical Anomaly Detection Based on Process-In-Memory Chip

Neural-networks (NNs) for the current feature analysis bring novel electrical safety functions in smart circuit breakers (CBs), especially for preventing the fire hazard from electric vehicle/bike battery charging. In this work, the edge artificial intelligence (AI) solutions for the electrical anomaly detection were designed and demonstrated based on the process-in-memory (PIM) AI chip. The ultra-low power and high-performance character of PIM AI chips enable the edge solution to embed in the limited space inside the circuit breaker and to detect improper battery charging at millisecond latency.

Integrating Generative AI into the Software Development Lifecycle: Impacts on Code Quality and Maintenance

Recent advances in generative AI have depicted it as a revolutionary approach in the software development technologies pioneered to improve the codes' reliability and sustain their quality and performance. Generative AI tools can help develop code independently, suggest intelligent solutions and ideas, and enhance several development procedures thanks to superior algorithms and machine learning features. This paper discusses how generative AI can/has been applied within software development to achieve the following three goals: First, to increase the code quality using automated code generation/review. Second, the code maintainability should be improved through standards and documentation. Third, to increase the up-to-speed development productivity due to AI-based automation, namely the automation of repetitive tasks and fast prototyping. The paper also considers issues and difficulties that can be tied to AI in this context: problems of dependence on AI, ethical and security issues, and technical imperfections. Finally, the implications of generative AI in software development in the future are presented, which can open a new direction in the development of software products while primarily pointing to the processes of managing the introduction of generative AI. Using the evaluation of the current possibilities and future perspectives of generative AI presented in this paper, one can conclude its impact on the future of software engineering.

Optimization Analysis of Tennis Player Training Mode Based on 3D Simulation Model

This paper is based on 3D simulation technology and deeply analyzes the optimization strategy of tennis player training mode. The research focuses on two core systems: one is a remote motion monitoring system based on architecture, which captures and analyzes athletes’ motion data in real time, providing coaches with accurate motion feedback and training effectiveness evaluation; The second is a 3D-based safety monitoring system, which uses 3D simulation technology to simulate the training environment, monitor and warn potential safety risks in real time, and ensure the safety of athletes during training. These two systems are designed for different demand scenarios in tennis training, with complementary functions, and together constitute an intelligent solution for optimizing the training mode of tennis players. By continuously improving algorithms, optimizing data storage and computation methods, and improving the accuracy of data analysis and mapping, this study aims to enhance the scientific, efficient, and safe training of tennis players, providing strong support for the comprehensive improvement of their skills and physical fitness.

Research on Intelligent Agricultural Monitoring and Control System Based on Neural Networks in the Internet of Things Environment

In today's rapidly advancing information technology landscape, the Internet of Things (IoT) has profoundly impacted traditional agriculture. IoT technology, as an intelligent solution, enables real-time data collection, processing, and analysis, significantly enhancing the efficiency and intelligence of agricultural production. Among these advancements, the neural network-based intelligent agricultural monitoring system stands out. This system not only monitors the agricultural production environment in real-time but also employs deep learning algorithms to predict future trends, guiding agricultural practices to achieve precision farming. Therefore, exploring the intelligent agricultural monitoring and control system based on neural networks in the IoT environment holds significant importance.

A review of deep learning techniques for enhancing spectrum sensing and prediction in cognitive radio systems: approaches, datasets, and challenges

Cognitive radio (CR) is an emerging wireless technology designed to optimize frequency band usage and address spectrum shortages. Spectrum sensing and prediction are crucial for cognitive radios to make intelligent spectrum access decisions and dynamically alter transmission parameters based on real-time spectrum conditions. These techniques contribute to the efficient use of the spectrum. However, they encounter significant obstacles such as noise uncertainty, low signal-to-noise ratios, channel fading, and more, necessitating robust and intelligent solutions. Deep learning has attracted much attention and displayed great potential in various fields in recent years. This review paper provides a thorough examination of the use of deep learning techniques in spectrum sensing and prediction in the context of cognitive radio. It examines the available literature in depth, highlighting the techniques, the assessment keys, datasets, and limitations of the investigations. The article seeks to provide significant insights and guidance for future developments in harnessing deep learning for better cognitive radio spectrum management by critically assessing the present state of research.

BIM and IFC Data Readiness for AI Integration in the Construction Industry: A Review Approach

Building Information Modelling (BIM) has been increasingly integrated with Artificial Intelligence (AI) solutions to automate building construction processes. However, the methods for effectively transforming data from BIM formats, such as Industry Foundation Classes (IFC), into formats suitable for AI applications still need to be explored. This paper conducts a Systematic Literature Review (SLR) following the PRISMA guidelines to analyse current data preparation approaches in BIM applications. The goal is to identify the most suitable methods for AI integration by reviewing current data preparation practices in BIM applications. The review included a total of 93 articles from SCOPUS and WoS. The results include eight common data types, two data management frameworks, and four primary data conversion methods. Further analysis identified three barriers: first, the IFC format’s lack of support for time-series data; second, limitations in extracting geometric information from BIM models; and third, the absence of established toolchains to convert IFC files into usable formats. Based on the evidence, the data readiness is at an intermediate level. This research may serve as a guideline for future studies to address the limitations in data preparation within BIM for AI integration.

How large language model-powered conversational agents influence decision making in domestic medical triage contexts

IntroductionEffective delivery of healthcare depends on timely and accurate triage decisions, directing patients to appropriate care pathways and reducing unnecessary visits. Artificial Intelligence (AI) solutions, particularly those based on Large Language Models (LLMs), may enable non-experts to make better triage decisions at home, thus easing the healthcare system's load. We investigate how LLM-powered conversational agents influence non-experts in making triage decisions, further studying different persona profiles embedded via prompting.MethodsWe designed a randomized experiment where participants first assessed patient symptom vignettes independently, then consulted one of the two agent profiles—rational or empathic—for advice, and finally revised their triage ratings. We used linear models to quantify the effect of the agent profile and confidence on the weight of advice. We examined changes in confidence and accuracy of triage decisions, along with participants' perceptions of the agents.ResultsIn a study with 49 layperson participants, we found that persona profiles can be differentiated in LLM-powered conversational agents. However, these profiles did not significantly affect the weight of advice. Notably, less confident participants were more influenced by LLM advice, leading to larger adjustments to initial decisions. AI guidance improved alignment with correct triage levels and boosted confidence in participants' decisions.DiscussionWhile LLM advice improves triage recommendations accuracy, confidence plays an important role in its adoption. Our findings raise design considerations for human-AI interfaces, highlighting two key aspects: encouraging appropriate alignment with LLMs' advice and ensuring that people are not easily swayed in situations of uncertainty.

Innovative Data Visualization for Business Intelligence: Enabling Ad-hoc Querying and Predictive Analysis

This comprehensive analysis explores the transformative impact of innovative Business Intelligence (BI) solutions that combine ad-hoc querying capabilities with advanced predictive analytics. The article examines the limitations of traditional BI tools and highlights the critical need for more flexible and powerful solutions in today's fast-paced business environment. It delves into the key features of modern BI solutions, including interactive visualizations, natural language processing, automated data preparation, multi-source data integration, and collaborative features. The study also investigates the far-reaching effects of these advanced BI tools on various aspects of business operations and strategy, such as operational efficiency, strategic planning, customer-centric approaches, risk mitigation, and innovation. Supported by extensive industry data and research, this analysis demonstrates how these cutting-edge BI solutions are reshaping the way organizations interact with data, make decisions, and compete in an increasingly data-driven marketplace.

Research on Smart City Road Network Capacity Optimization Configuration Based on Deep Learning Algorithms

At present, the urban traffic system is faced with the problems of congestion and low efficiency, and the traditional methods have certain limitations when dealing with the complex urban road network. This paper aims to explore a new method of capacity Optimization of Road Networks in a Smart City environment and proposes a method based on a Graph Neural network, named “Smart City Road optimization Graph Neural Network” (SCRO-GNN). SCRO-GNN first collects and preprocesses multi-source data, including road network data, traffic flow, accident records, environmental factors, etc. The key node and edge features, including the number of lanes at the intersection, the traffic flow of the section, and the adjacency matrix of the road network are defined to characterize the structure of the road network. Then, the graph neural network model is constructed and trained to predict the traffic flow of different sections and evaluate the road capacity by using the graph structure of the road network. This paper tests the performance of SCRO-GNN on real road networks in multiple cities. The results show that, compared with the traditional traffic flow prediction model, SCRO-GNN can significantly improve the prediction accuracy, especially when dealing with the highly complex urban road network structure. Based on these predictions, the proposed optimization strategy performed well in reducing traffic congestion and improving the efficiency of road use. The research in this paper not only demonstrates the potential of graph neural networks in smart city road network optimization, but also provides a new direction for future traffic system research. The successful implementation of the SCRO-GNN approach is expected to provide more efficient and intelligent solutions for urban traffic management and planning.

Rhythm-Ready: Harnessing Smart Devices to Detect and Manage Arrhythmias.

To survey recent progress in the application of implantable and wearable sensors to detection and management of cardiac arrhythmias. Sensor-enabled strategies are critical for the detection, prediction and management of arrhythmias. In the last several years, great innovation has occurred in the types of devices (implanted and wearable) that are available and the data they collect. The integration of artificial intelligence solutions into sensor-enabled strategies has set the stage for a new generation of smart devices that augment the human clinician. Smart devices enhanced by new sensor technologies and Artificial Intelligence (AI) algorithms promise to reshape the care of cardiac arrhythmias.