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
Background: Private cloud infrastructures offer organizations the benefits of cloud computing with enhanced control over data, compliance, and customization. However, optimizing these environments remains a challenge due to complexities in resource management, scalability, and cost-efficiency. Objective: This study aims to identify and evaluate strategies for optimizing private cloud performance, reducing operational costs, and enhancing scalability while maintaining robust security and compliance. Methods: The research focuses on key techniques such as resource provisioning optimization, the implementation of Infrastructure as Code (IaC), containerization, hybrid cloud integration, and AI-driven workload management. It also examines case studies and real-world implementations to validate the effectiveness of these strategies. Results: Findings highlight that a combination of automation, proactive resource scaling, and hybrid cloud synergy significantly improves private cloud efficiency. Organizations implementing these strategies report improved resource utilization, reduced latency, and streamlined operations. Conclusion: By adopting best practices and leveraging modern technologies, organizations can optimize their private cloud environments to achieve a balance between performance, cost, and security. These insights are crucial for IT leaders seeking to maximize the value of private cloud deployments in an increasingly competitive and regulated landscape.
Copyright (c) 2025 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
Real-Time Analytics and Machine Learning in Agriculture for Biodiversity Conservation
In recent years, the agriculture industry has embraced artificial intelligence (AI) to maximize crop yields. While this has led to improved productivity, it often comes at a cost—local biodiversity. Our research focuses on a novel approach to bridge this gap by developing a comprehensive framework that uses machine learning (ML) and real-time data analytics to balance agricultural productivity with biodiversity conservation. This study integrates insights from three robust datasets—AgData Commons, NOAA Climate Data, and the Global Biodiversity Information Facility (GBIF)—to create a decision-making tool designed for farmers. By leveraging these diverse datasets, we analyze key parameters like soil health, climate conditions, and biodiversity metrics. The result is a system that not only enhances crop yield but also promotes the health of surrounding ecosystems. Our findings demonstrate the significant potential of this approach to reshape agricultural practices by fostering harmony between productivity and conservation. This research has implications that go beyond farming, influencing agricultural management policies and offering a new perspective on sustainable development.
Based- Knowledge Block Lattice Filters for an MTI System
This paper researches adaptive Stagger-Block-Lattice-Filter (SBLF) processor, which can be applied to an MTI radar. Our years’ experiments prove the necessity to incorporate AI into the processor. Only algorithmic operation largely restricts clutter suppression performance in complex environments. The high speed and small radar-cross-section of a target compel an MTI to be upgraded by stagger PRI (Pulse Repetition Interval), PC (Pulse Compression) transmissions, and intelligent operation. Five heuristic strategies for knowledge-based adaptive SBLF processor are proposed 1) Non-clutter block decision and threshold set-up, which decides whether the Test block to be dominated by a clutter and uses the filtering-itself output to set up detection thresholds. 2) Target block identification and terrace-indication declaration, which identifies the Test block to be dominated by a target or not and determines PC terrace-indication. 3) SBLF coefficient estimation, which calculates SBLF coefficients in the Guard I block in real-time. 4) Establishment of clutter-map, which stores the thresholds, target block index, terrace-indication index, SBLF coefficients and notch parameters in the corresponding map cells. 5) Guidance of stagger PRI, which collects target block indices from adjacent bearings and notch parameters to assist target extraction and to suggest the available PRI. In order to verify the effectiveness of the above intelligent operations, we do a lot experiments with computer and select a ship-borne radar as the background; the clutter returns feature the land, sea and weather environments and target returns feature weak high speed aircrafts. No matter in homogeneous or in heterogeneous clutters, this processor demonstrates the ability to extract the weak aircrafts in the strong clutter.
Enhancing Cloud Security and Compliance through Integrated GRC and IAM Frameworks
As enterprises increasingly adopt cloud technologies, the integration of robust Governance, Risk Management, and Compliance (GRC) with Identity and Access Management (IAM) becomes crucial to safeguard sensitive data and ensure regulatory compliance. With over eight years of experience as a Cloud IAM Engineer specializing in Microsoft Azure, my expertise lies in deploying secure, scalable, and compliant cloud solutions. This presentation will outline effective strategies for integrating GRC frameworks with IAM protocols within cloud architectures, emphasizing tools and techniques that enhance security without compromising flexibility or scalability. I will explore the challenges and solutions associated with migrating to cloud environments while maintaining compliance with rigorous standards such as FedRAMP, SOC 2, and PCI-DSS.
Advanced Data Engineering, Analytics, and AI: Strategies for Innovation and Efficiency
In today’s data-centric landscape, the ability to effectively manage and utilize vast amounts of data is paramount for fostering innovation and enhancing operational efficiency. This expert note, presented by Preyaa Atri, a seasoned Senior Data Engineer with over a decade of experience, delves into the sophisticated strategies and methodologies in data engineering, analytics, and AI that drive success across diverse industries. Drawing on her extensive professional background and numerous published research works, Preyaa will provide a comprehensive and detailed exploration of the following key areas:
L. D. Landau described the quantum liquids He3 and He4 at low temperature (below 3K) by applying the quasi-particle approach called the quasi particles (elementary excitations) as “rotons”. We discuss in this paper how these physical materials may be candidates for building the processor of a quantum computer of type based on the quantum set theory of G. Takeuti.
Optimized Asynchronous FPGA Designs for Efficient AI Acceleration
This work presents a connection between asynchronous circuits and artificial intelligence (AI), with a particular focus on the implementation and optimization of asynchronous designs in Field-Programmable Gate Array (FPGA) architectures. It is demonstrated how low-level asynchronous circuits can be designed with the specific intention of being used with FPGAs. This allows the inherent advantages of asynchronous systems, such as reduced latency and energy efficiency, to be utilized for AI workloads. FPGAs are employed in this context as accelerating hardware platforms, which represent a promising solution for AI applications due to their flexibility and performance. The results illustrate the potential of asynchronous FPGA designs as efficient accelerators for AI and open up new avenues for the development of energy-efficient, high-performance hardware solutions in artificial intelligence.
Agentic AI for Modern Financial Operations: A Blueprint for Intelligent and Autonomous Workflows
Modern financial operations are becoming increasingly complex as organizations manage high transaction volumes, diverse data inputs, and heightened demands for speed, accuracy, and compliance. Traditional batch-driven, rules-based systems often struggle to meet these expectations. Agentic AI offers a new approach by enabling intelligent, autonomous agents to interpret unstructured information, coordinate tasks, and support complex decision-making with minimal human intervention. This presentation introduces a high-level blueprint for applying Agentic AI to streamline financial workflows, enhance operational intelligence, and strengthen decision cycles. It highlights how multi-agent coordination, cloud integration, data governance, and responsible AI principles can deliver safer, more adaptive, and real-time operational capabilities—while working seamlessly with existing enterprise systems and regulatory frameworks.
Challenges of AI in Intelligence Analysis: A 2025 Perspective
Artificial intelligence (AI) is revolutionizing intelligence analysis by enabling real-time insight generation and large-scale data synthesis. However, its integration presents substantial hurdles involving explainability, bias, data reliability, new security vulnerabilities, regulatory ambiguity, reasoning limitations, and the challenge of legacy system modernization. This paper examines each challenge systematically and proposes practical recommendations to facilitate responsible, secure, and effective AI adoption in intelligence ecosystems.
Building Intelligent Workflows with the Model Context Protocol (MCP): Servers, Clients, and Extensible Tools
The Model Context Protocol (MCP) is transforming how AI systems interact with enterprise data, tools, and applications by establishing a standards-based, secure, and extensible communication layer between LLMs and external resources. This presentation introduces the core architecture of MCP, explains how servers expose structured capabilities, how clients (including Claude Desktop, custom apps, or headless agents) consume these capabilities, and how developers can build or integrate extensions-such as file systems, databases, APIs, automation frameworks, or custom logic. Attendees will gain a practical understanding of message formats, transport layers, capability registration, authentication models, session state, and best practices for designing robust MCP servers. Real-world use cases-including workflow automation, data orchestration, RAG pipelines, and developer productivity tooling-demonstrate how MCP enables LLMs to execute actions reliably and safely. The talk concludes with hands-on patterns and design guidelines for creating scalable, secure, and production-ready MCP ecosystems.
AI, Data, and Security: Shaping the Future of Intelligent and Trusted Systems
Security, Decision-Making, and Self-Healing Systems: Artificial Intelligence (AI) is transforming how we secure, manage, and trust the digital world. This session explores the convergence of AI, data, and cybersecurity in creating intelligent and self-healing cloud systems. Drawing from real-world implementations, it highlights how Generative AI, machine learning, and deep learning are powering autonomous threat detection, predictive remediation, and adaptive decision-making in hybrid cloud environments. As cyber risks evolve faster than human response, AI-driven observability and automation are redefining resilience - enabling infrastructures that can learn, adapt, and recover in real time. The talk reveals how emerging models such as LLMs and GANs drive this shift from reactive defense to proactive trust. Attendees will gain insight into the next frontier of autonomous, explainable, and data-centric security shaping the intelligent systems of tomorrow.
The present article provides a theory of dark energy that appears to have been developed in two complementary ways. On the one hand, this theory is based on physics and mathematics and, on the other hand, it is developed on the basis of available data. This corresponds to the discovery of the laws of planetary motion in elliptical planetary orbits by JOHANNES KEPLER in the past. He developed his laws from a large amount of data. Later it was theoretically substantiated more thoroughly by ISAAK NEWTON.
AI-Augmented Cyber Defense: Reimagining Security in the Age of Intelligent Threats Chemotherapy
As enterprises embrace artificial intelligence (AI) for operational efficiency, the same technology is increasingly weaponized by adversaries to launch sophisticated, adaptive, and autonomous attacks. This session explores how AI-driven systems are reshaping both offense and defense in the cybersecurity landscape. Drawing from real-world enterprise implementations across healthcare and retail, the keynote examines how predictive analytics, large language models, and cognitive automation are transforming threat detection, identity management, and incident response. It also delves into the emerging paradigm of “adaptive identity,” where AI continuously learns from user behavior to secure access in hybrid environments. The talk emphasizes the ethical and governance dimensions of AI in cybersecurity, offering a roadmap for building explainable, resilient, and trustworthy digital ecosystems. Attendees will gain actionable insights into leveraging AI not only as a defensive capability but as a catalyst for secure innovation and enterprise trust.
Inside the Mind of a Self-Driving Car: Engineering Intelligence on Wheels
Self-driving cars combine cameras, sensors, and intelligent computer programs to move safely without human help. These systems are designed to see the road, understand what is happening, and make quick driving decisions. This paper explains how self-driving cars work step by step — starting with computer vision and deep learning for lane detection, traffic sign recognition, behavior cloning, and object tracking. It also covers sensor fusion, localization, and control techniques such as Kalman filters and PID controllers that help the car know its position and move smoothly. The paper further explains path planning, system integration, and safety methods that allow all components to work together. Finally, it discusses the challenges, current progress, and future direction of autonomous driving. The goal is to help readers clearly understand how modern cars think, plan, and act intelligently on the road.
Architecting AI Workflows with Apache Spark Chemotherapy
This session traces the evolution of big data systems - from Hadoop’ s batch-driven model to modern distributed architectures - and explores how AI-driven approaches can enhance and optimize Apache Spark. We’ll break down Spark’s internal design, including the roles of the Driver, DAG Scheduler, Task Scheduler, and Executors, to show how large-scale workloads are processed efficiently across clusters. Using real-world examples like cost aggregation pipelines, the talk highlights how Spark overcomes Hadoop’s limitations while still facing challenges around configuration complexity, data skew, and resource management. Finally, we’ll discuss how reinforcement learning can be applied to Spark to enable dynamic scheduling, smarter partitioning, and adaptive resource allocation, transforming Spark into a self-optimizing data processing engine.
Uncovering Hidden Gambling Networks in the Philippines Using AI-Driven Web Similarity Analysis
The rapid expansion of online gambling in the Philippines has made it difficult for regulators to keep track of unlicensed operations. This presentation introduces an AI- and ML-powered framework that identifies related or cloned gambling sites through web similarity analysis. This helps track and block these illegal gambling sites as soon as they come online. The system analyses digital fingerprints to build feature vectors representing each domain’s online identity. Using similarity algorithms, the framework uncovers networks of domains sharing common infrastructure or content lineage. Application of this approach led to the discovery of a significant number of previously unlisted or disguised gambling sites linked to known operators. The session details the feature engineering process, pipeline automation, and real-world findings, demonstrating how AI-driven web similarity analysis enhances cyber threat intelligence and strengthens regulatory oversight of the online gambling ecosystem.
AI for Access Control Policy Mining: Challenges and Opportunities
AI for Access Control Policy Mining: Challenges and Opportunities Access control plays a central role in securing modern information systems, but designing effective policies remains a difficult and time-consuming task. Traditional policy mining approaches face challenges such as limited datasets, incomplete information, and the difficulty of balancing accuracy with interpretability. These obstacles make it difficult to develop policies that are both precise and understandable, particularly in large or dynamic systems. Recent advances in AI provide new opportunities to address these challenges. AI methods can assist in discovering patterns in permissions, generating candidate policies, and refining rules to achieve greater accuracy and clarity. Moreover, AI can help automate parts of the policy mining workflow, reducing manual effort and enabling researchers and practitioners to handle more complex systems. At the same time, introducing AI into this process brings new challenges: ensuring correctness, avoiding overfitting to noisy data, and maintaining transparency and trustworthiness in AI-assisted policy generation. This talk will explore the promise and pitfalls of applying AI to the policy mining problem. We will discuss how AI techniques can support the discovery of high-level access control rules, highlight the risks of relying on opaque models in security-critical contexts, and outline future directions for integrating AI into access control research and practice.
AI-The Game changer for Agile Workflows: AI is transforming Agile processes by integrating predictive understandings and automated, real-time adjustments. Agile lives and breathes quick iterations, constant feedback and adaptability - core principles that intertwine seamlessly with AI. By fusing machine learning, natural language processing and intelligent automation to boost sprint planning, backlog refinement and team synergy to a level of accuracy and efficiency that simply astonishes. Thanks to AI, it’s now possible to derive predictive understandings from historical sprint data and team performance metrics. Automated systems for testing and bug detection as well as user story generation minimize the demand for manual work. Thanks to real-time performance monitoring and AI-generated reports, Scrum Masters and Product Owners can act faster and smarter. Not only does it transform team interactions and project planning, but also execution and product rollout. When AI-enhanced agile practices are implemented, an organization reaches the next level in its efficiency and implementation scale. The paper explores the intersection of AI and Agile pinpointing the use cases and tools while suggesting implementation strategies that make AI a real game changer in modern Agile workflows.
On the Convergence of the Euler-Maruyama Scheme for McKean-Vlasov SDEs
On the convergence of the Euler-Maruyama scheme for McKean-Vlasov SDEs: Building on the well-posedness of the backward Kolmogorov partial differential equation in the Wasserstein space, we analyze the strong and weak convergence rates for approximating the unique solution of a class of McKean-Vlasov stochastic differential equations via the Euler-Maruyama time discretization scheme applied to the associated system of interacting particles. We consider two distinct settings. In the first, the coefficients and test function are irregular, but the diffusion coefficient remains non-degenerate. Leveraging the smoothing properties of the underlying heat kernel, we establish the strong and weak convergence rates of the scheme in terms of the number of particles $N$ and the mesh size $h$. In the second setting, where both the coefficients and the test function are smooth, we demonstrate that the weak error rate at the level of the semigroup is optimal, achieving an error of order $N^{-1} + h$.
Copyright (c) 2026 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
The Impact of Socio-Emotional Education on Students’ Academic Performance
In an educational context that is continuously evolving, socio-emotional education has gained increasing recognition as a key contributor to student achievement. Beyond cognitive skills, socio-emotional competencies play a crucial role in shaping students’ motivation, engagement, and overall well being. This study examines the relationship between socio-emotional learning (SEL) and academic performance, offering evidence-based insights into effective teaching practices. Using a mixed-methods design, the research combines student surveys, teacher interviews, and an extensive literature review to build a comprehensive understanding of SEL’s impact. Preliminary expectations suggest that students who participate in SEL programs demonstrate stronger academic outcomes, higher levels of motivation, and more active classroom involvement. Teacher interviews are expected to identify successful strategies, highlight implementation challenges, and propose practical ways to integrate SEL into daily instruction. The findings aim to provide actionable recommendations for educators and policymakers, bridging theoretical frameworks with classroom realities. By nurturing socio-emotional skills, schools can create inclusive, supportive, and motivating learning environments that enhance academic success while fostering resilience, self-awareness, and interpersonal competence. This study underscores the transformative potential of socio-emotional education in promoting both academic excellence and holistic student development.
Socioemotional Education, Academic Performance, Motivation, Emotional Regulation, Engagement, Holistic Development
Bim in Public Investment Projects: Adoption, Impact, Policies and Future Directions
The digital transformation of the public construction sector is a strategic imperative for governments seeking to optimize resources and make processes transparent. Building Information Modeling (BIM) emerges as a central methodology in this transition, integrating multidisciplinary data to manage projects throughout their life cycle. Globally, the BIM market reached USD 8.85 billion in 2024, projecting growth of 11.2% annually until 2034. In Latin America, eight countries make up the BIM Network of Latin American Governments, evidencing a regional commitment to accelerate the digitalization of the public sector. Methodology, this study adopts a systematic review approach, analyzing literature indexed in high-impact scientific databases (Scopus Q1-Q2), complemented by technical reports from multilateral organizations such as the IDB and recent sectoral reports (2021-2025). Implementation experiences in Spain, Chile, Brazil and Peru were examined, identifying adoption patterns, persistent barriers and critical success factors through multilevel comparative analysis. Results and Discussion, empirical evidence demonstrates significant quantifiable impacts: reduction of deadlines between 15-25%, reduction of costs of 10-30%, and elimination of design errors by up to 70% through early detection of interferences. Spain recorded a 230% increase in BIM tenders between 2017-2020 after implementing mandatory mandates, while Chile consolidated its regional leadership through the BIM Plan integrated with academia and industry. However, substantial barriers persist in emerging economies: high initial investment, shortage of skilled professionals (affecting 50-65% of organizations), and outdated contractual frameworks that hinder collaboration. 4D/5D BIM applications demonstrate transformative potential, reducing unbudgeted costs by up to 40% and integrating time scheduling with real-time cost estimation. Digital twins represent the technological frontier, with the potential to improve public sector efficiency by 20-30%. Conclusions, the successful adoption of BIM in public investment requires holistic strategies that combine regulatory mandates, economic incentives, professional capacity building, and collaborative contractual frameworks. Context-specific policies, university curriculum integration, and standardization through ISO 19650 are key pillars for overcoming regional disparities and maximizing the return on investment in public infrastructure.
The Governance of Intelligence: Scaling Trusted Data through Machine Learning, Artificial Intelligence, and Large Language Models
As Machine Learning (ML), Artificial Intelligence (AI), and Large Language Models (LLMs) evolve toward increasingly autonomous systems, the integrity, governance, and trustworthiness of data have become decisive factors in determining real-world effectiveness. Traditional data governance approaches largely manual, reactive, and compliance-driven-are insufficient to support the scale, velocity, and heterogeneity of modern intelligent systems. In regulated domains such as healthcare, these limitations manifest as model hallucination, bias amplification, regulatory non-compliance, and escalating technical debt. This paper proposes a governance-by-design framework that synergistically integrates ML, AI, and LLMs to enable scalable, proactive, and explainable data governance. ML automates data profiling, quality assessment, and bias detection; AI enables predictive stewardship through risk forecasting, anomaly detection, and autonomous policy enforcement; and LLMs provide semantic governance by interpreting unstructured data and regulatory text in natural language. The framework is validated through a longitudinal health informatics case study focused on disease recurrence prediction using integrated clinical and administrative data. Embedding governance directly into the ML pipeline resulted in a 32% improvement in model fairness, measured via Average Odds Difference (AOD), a 50% reduction in deployment latency, and a measurable reduction in technical debt, operationalized as decreased rework cycles, reduced schema violations, and faster model redeployment. We conclude by introducing the Autonomous Data Officer (ADO)-a human-supervised, AI-driven governance control plane that transforms governance from a post hoc auditing function into a real-time enabler of trusted, enterprise-scale intelligence. This work directly supports the ICMLAIDS 2026 theme of synergizing intelligence across ML, AI, and Data Science for a smarter tomorrow.
Data Governance, Trusted AI, Machine Learning, Artificial Intelligence, Large Language Models, Health Informatics, Predictive Analytics, Explainable AI
Future of Machine-to-Brain Communication: Listen Deposits
Brain–computer interfaces (BCIs) offer new avenues for communication and control, but current technologies are limited by low bandwidth, invasiveness, and reliance on traditional sensory pathways. This paper proposes a novel paradigm of machine-to-brain communication using AI-modulated electromagnetic waves to transmit data directly into the brain’s neural circuitry. We outline the theoretical basis for this approach, drawing on neuroscience and electromagnetic theory: specifically, how external radiofrequency (RF) signals (in the 5G spectrum) might influence neural activity by modulating the dynamics of sodium ions that underlie action potentials. A concept termed listenDeposits is introduced to describe auditory-like perceptual experiences induced without any physical sound, analogous to hearing a “broadcast” directly in the mind. We review background literature on brainwaves, neural signaling, and prior uses of AI in neural stimulation to ground our proposal in existing science. We then present a framework in which AI-driven 5G-wave modulation could entrain neural firing patterns and encode information non-invasively, and we explore how factors like signal amplitude and proximity could enable shared, collective perceptual experiences among groups (a “wireless” group BCI effect). Ethical considerations are discussed in depth, emphasizing the need for transparency, informed consent, and human-centric oversight as this technology develops. Finally, recognizing the speculative nature of our proposal, we outline experimental approaches to validate these ideas in future work- including the use of sodium-sensitive MRI and EEG to detect wave-induced neural synchronization. This paper lays a conceptual foundation for AI-modulated machine-to-brain communication, aiming to inspire interdisciplinary research while underscoring the paramount importance of ethical and safe innovation in this emerging domain.
Cloud computing has revolutionized businesses' operations, providing scalable, cost-effective, and efficient data storage, processing, and management solutions. Integrating artificial intelligence (AI) within cloud environments has further enhanced optimization capabilities, particularly in resource allocation, security, and operational efficiency. This paper explores the leading cloud platforms, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), detailing their core features, advantages, and applications. Additionally, the study delves into cloud data warehousing, emphasizing Snowflake's role in enterprise analytics. AI-driven resource optimization is examined through auto-scaling, load balancing, and predictive analytics. Security considerations are also discussed, including AI-enhanced threat detection and compliance measures. The paper concludes with an outlook on the future of cloud computing, highlighting AI's expanding role in business operations.
Cloud Computing, Artificial Intelligence, AWS, Azure, GCP, Cloud Security, AI Optimization, Data Warehousing, AutoScaling, Predictive Analytics, Machine Learning
Conversational AI for Disability Inclusion: Designing Accessible andEquitable Interaction Systems
Conversational Artificial Intelligence (AI) has emerged as a transformative tool in promoting accessibility and inclusivity for individuals with disabilities. By leveraging voice and text interactions, systems such as chatbots, virtual assistants, and speech recognition technologies can mitigate barriers faced by people with disabilities (PWD) in communication, education, and daily activities. This narrative review synthesizes findings from recent research to explore the applications, challenges, and ethical considerations of conversational AI in enhancing accessibility. Framed by the Social Model of Disability, the analysis highlights how design choices can either remediate or exacerbate societal barriers. Key themes include multimodal interfaces, personalized interactions, bias mitigation, and user-centered design. Drawing on case studies and theoretical frameworks, I propose strategies for designing equitable conversational AI systems and conclude with recommendations for future research and development, emphasizing the need to address economic and infrastructural barriers to ensure these technologies serve diverse populations effectively
Conversational AI, Accessibility, Inclusivity, Voice Interactions, Text Interactions, Disabilities, Social Model of Disability, Participatory Design
TMAK; Emotion Estimation, Mental Health Application, Information Recommendation - Application for Stress Estimation Model and Mild Cognitive Impairment Detection
TMAK (Trustworthy Multimodal Affective Intelligence and Knowledge Engineering Laboratory) is advancing research on several key themes. First, we are researching methods to estimate human emotions from multimodal information (audio, language, images) and apply this to support the diagnosis of mental disorders, mental health conditions, and dementia. Second, we are researching techniques to analyze interest and reputation information from diverse web reviews and social media posts and utilize it for information recommendation. Third, we are developing empathetic dialogue systems by leveraging the rapidly advancing large-scale multimodal language models. All these research areas require feature extraction from large-scale data, making big data analysis platforms indispensable. Our laboratory is also advancing research on lightweight AI models, developing language models, emotion estimation, and stress estimation algorithms capable of running on local edge devices. This presentation will introduce past research examples, outline solutions using our proprietary emotion estimation technology based on multi-stage fine-tuning, and discuss future prospects such as cognitive function prediction.
Evolving Attribution Models in Digital Advertising: Balancing Clicks,Views and Customer Journeys
As digital advertising continues to grow, accurately measuring the impact of marketing efforts across multiple channels has become a complex but essential task. Traditional attribution models like last-touch fail to capture the diverse ways consumers interact with ads before converting. Multi-touch attribution (MTA) models offer a solution by distributing credit across various interactions, reflecting the true influence of each touchpoint. This paper provides a comprehensive review of popular attribution models, including linear, time-decay, U-shaped, and custom models, assessing their strengths and limitations. Additionally, it highlights the importance of integrating both click-through and view-through metrics to understand customer engagement more fully. By exploring these models, this paper aims to guide marketers in selecting the most effective attribution strategies to optimize ad spend, improve campaign performance, and gain a holistic view of customer journeys.
Copyright (c) 2024 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
Advancing Moving Target Strategy with Bio-Inspired Reinforcement Learning to Secure Misconfigured Software Applications
Misconfigurations in software systems are a persistent source of security vulnerabilities, particularly within static architectures that fail to adapt over time. Moving Target Defense (MTD) offers a proactive approach by dynamically altering the system’s attack surface, thereby reducing exposure. This paper builds upon an MTD model, RL-MTD, which leverages Reinforcement Learning (RL) to generate adaptive secure configurations. Although effective, RL-MTD faces limitations due to an unoptimized and sparse search space. To address this, two hybrid models—GA-RL and PSO-RL—are proposed, integrating Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into the RL-MTD framework. Experiments on four misconfigured SUTs show both models outperform the baseline. Notably, PSO-RL yields the most secure configurations in most scenarios. The authors present a prototype demonstrating how PSO-RL could be applied on a constrained Windows 10 system to defend against an attack. These findings enhance MTDbased adaptive cybersecurity via optimized search.
A Method to Test the Ethics of Some AI Classifiers - The Example of School Dropouts Problem
If an AI artifact is an emulation of human behavior in relation to the performance of some activity and if the human being, in carrying out that activity, is required to respect a behavioral framework defined by laws, rules, regulations, procedures, best practices, etc., then the AI that emulates that human behavior is also required to respect the same behavioral framework. The idea of ethics tests is developed on this principle and, precisely on the basis of this principle, apragmatic methodology can be developed that can test the correspondence in the observance of the artefact to the behavioral framework within which it will necessarily be placed in its operation. The approach proposed in this work allows us to offer an extremely pragmatic solution to the search for an “ethical behavior” for AI artifacts, bypassing the difficult applicability of the complex and abstract legislation currently in force on this topic. In order to explain the applicability of this methodology to a concrete problem, this work considers theproblem of school dropout as an example and describes the construction of two classifiers, one based on a neural network and one on a decision tree, able to predict the phenomenon. The application of the methodology clearly shows how the explainability offered by a symbolic system, such as a decision tree, is not applicable as an element of explainability in the behavior of a neural classifier.