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
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.
Copyright (c) 2025 Journal of Artificial Intelligence & Cloud Computing https://creativecommons.org/licenses/by/4.0
International Conference on Artificial Intelligence and Cloud Computing (ICAICC-2025)
Artificial Intelligence and Cloud Computing
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.
Dynamic Risk-Based Authentication Using AI Scoring Models in Healthcare Applications
As digital healthcare systems grow increasingly complex and globally interconnected, safeguarding patient data while ensuring seamless access has become a critical challenge. Traditional authentication methods, such as static passwords and inflexible multi-factor authentication (MFA), often fail to provide context-aware security without disrupting usability. This paper presents a novel Dynamic Risk-Based Authentication (RBA) framework that integrates AI-powered risk scoring into a Healthcare Application developed on the Oracle APEX 24.2 low-code platform. The proposed solution evaluates real-time contextual data such as device fingerprinting, geolocation, behavioral history, and temporal patterns during each login attempt. This data is transmitted to an OpenAI-powered inference engine via RESTful APIs, which returns a normalized risk score. Depending on the risk classification (low, medium, or high), the application dynamically adjusts authentication mechanisms ranging from seamless login to OTP verification or full access denial. This approach demonstrated a 35% reduction in false positives and effectively blocked 92% of high-risk login attempts in pilot studies within healthcare domains. The architecture also includes adaptive learning feedback loops, enabling model refinement over time. Designed for regulatory compliance (HIPAA, GDPR) and operational scalability, this AI-RBA framework proves that low-code platforms can deliver enterprise-grade, context-sensitive cybersecurity solutions. This research contributes a scalable, cross-domain authentication paradigm that enhances both data security and user experience, addressing a key challenge in AI-enhanced cybersecurity systems.
From Principle to Process -Implementing Responsible AI Across National Borders
The design of responsible AI systems in line with international requirements requires a holistic approach. Environmental and social aspects should be integrated as early as the design stage and throughout all phases of the life cycle in order to comply with the principles of trustworthiness for AI. European regulations, in particular the AI-Regulation (EU AI-Act), set clear requirements for transparency, security, and accountability. Supplemented by international standards and norms, the path is being paved for greater responsibility and sustainability in the development and use of AI. Business models are thus being realigned globally toward sustainability and the integration of ethical and social aspects through the use of trustworthy AI.
An alternative way of quantum computing is presented. This approach is more general than the one based on the notion of the quantum bit. Mathematically it is based on the quantum set theory of Gaisi Takeuti. A possible implementation of this quantum computer belonging to this quantum computing could be to build up by centering it on the quantum liquids He3 and He4 at low temperature (below 3K) and by applying the quasi-particle approach of L.D. Landau.
Efficient Context Retrieval in Large Language Models Using Low Dimensional Embeddings
Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating human language, but their deployment in enterprise scenarios often faces challenges related to Cost, Scalability, and Efficiency. Training or fine-tuning large models on customer-specific data is typically resource-intensive, while embedding vectors with thousands of dimensions can be expensive to store and slow to retrieve at scale. This paper presents a Lean Approach to maximizing the value of LLMs with Minimal Training and Low-Dimensional Embeddings. We show how customer and field service data can be transformed into Dense Vectors of Reduced Dimensionality using unsupervised methods such as PCA or lightweight supervised projection heads trained on small labeled datasets. These compact embeddings (128–256 dimensions) are sufficient for high-quality semantic search, clustering, and classification while drastically reducing memory and compute requirements. The proposed architecture combines Vector Similarity Search (KNN, Cosine Similarity) with Retrieval-Augmented Generation (RAG), enabling LLMs to deliver context-Aware Responses without costly fine-tuning. Further accuracy is achieved with Lightweight Re-Ranking Models (e.g., logistic regression) trained on limited examples. We illustrate this workflow in a Field Service Use Case, where the system retrieves and synthesizes knowledge from past service tickets and manuals to provide technicians with actionable troubleshooting guidance. Our results highlight that Most Practical Gains in Enterprise LLM Applications Come from Retrieval Quality and Representation Efficiency Rather than Full Model Retraining. By focusing on compact embeddings and minimal additional training, organizations can achieve Faster Inference, Lower Costs, and Robust Performance in real-world customer support and field service scenarios.
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.
Integrating Salesforce Agentforce in Business: Challenges and Solutions
The launch of advanced AI agent platforms like Salesforce Agentforce represents a shift in enterprise ecosystems. This aims to meet the demand for digital labour. Embedded and autonomous agents can fit within workflows. Agentforce 2dx gives businesses the tools for better scalability. The paper looks at the technical, organizational, and ethical challenges of deployment in terms of reliability of system, integration into infrastructure, security, change management, workforce readiness, and regulatory compliance. It also talks about best practice architecture, data governance, security, change management, upskilling, and ethical AI frameworks. The aim is to help businesses implement Agentforce effectively to enjoy the benefits of AI.
Autonomous IoT Agents Powered by Generative Reasoning
The emergence of Generative Artificial Intelligence (GenAI) has unleashed its operational capabilities to bring about a revolution for many autonomous systems, especially those in the domain of the Internet of Things (IoT). This paper explores a new mechanism for promoting generative reasoning in autonomous IoT agents for dynamic, context-situated planning and decision-making. The agents use generative models to simulate highly intricate emerging scenarios of the environment and system and can react to them in real time. The demonstration of the framework on smart agricultural systems, where agents manage irrigation and pest control tasks autonomously on a preliminary basis, was highly encouraging in significant improvements of resource efficiency and yield productivity. The approach proposed here marries reinforcement learning, scenario simulation, and adaptive proactive mechanisms to rid most of the challenges facing the lately built reactive IoT framework. Hence, the agents imbued with generative reasoning can decide based not only on sensor data but rather also on predicted-and-anticipated outcomes, thus dealing with the changing scenarios with the appropriate strategy-making. The generative cognitive architecture shows utmost potential for transforming autonomous systems in agriculture, transportation, and energy sectors. Specific areas around multi-agent collaboration, secure deployment, and ethical issues regarding autonomous decisions in the future are elaborated in the presented study.
Autobiography of an Artificial Intelligence (AI): What Makes Humans and AI Different?
AI is constantly evolving. Its capabilities exceed those of humanity in many areas. If humans are reduced to reason and intellect, they have no chance.However, there are human characteristics that AI cannot develop: Gut feeling, heart, soul, emotions, intuition, inspiration, creativity, faith - love - hope,self-awareness, passions, spirituality, freedom. These topics are developed in dialog with an AI.
Universal Data Engineering Frameworks for Cross-Platform Fraud Detection
Developing, deploying, and operating on various platforms, including mobile banking apps and enterprise payment gateways, fraud detection systems must rise to meet the escalating threats presented by advanced fraud vectors. As a result, building a unified fraud detection system is challenging and necessary due to the heterogeneity of data sources, infrastructure ecosystems, and compliance standards. To address these challenges, this paper presents a generalized data engineering framework that enables scalable, platform-neutral, and real-time dynamic fraud detection through modular design, metadata-driven processing, and cross-platform interoperability. The framework brings together the structure, semi-structured, and unstructured data across a variety of systems through a common ingestion layer, standardized transformation pipelines, and abstracted machine learning integration points. In this paper, we assess modern technologies and patterns used in data engineering – e.g., schema-on-read ingestion, federated data access, event-driven processing, and cloud-native orchestration – to establish a common base that enables fraud analytics across diverse stacks. It promotes the use of DataOps practices toward automated pipeline deployment, lineage tracing, and incident tracing. Furthermore, this framework not only combines real-time streaming processing (using Apache Kafka, Spark Streaming, or Flink) with historical batch processing (using Delta Lake or Apache Iceberg) for real-time fraud response, but also long-term fraud patterns. It adds interoperability through data abstraction layers, RESTful APIs, and GraphQL endpoints, making the system's backend agnostic and allowing for custom interfaces to the analytical layers. Furthermore, the proposed framework utilizes adaptive learning methods to customize fraud detection rules and adapt to localized platform activities. This flexibility enables the architecture to be adopted in various domains, including fintech, insurance, healthcare, and e-commerce, where the sharing of knowledge through federated learning is made possible. This paper also discusses cross-jurisdictional data privacy, compliance (GDPR, HIPAA, PCI DSS), and regulatory reporting concerns by incorporating metadata-based policy enforcement and secure data masking techniques into the pipeline. A prototype for the framework was deployed in three domains-banking, ridesharing, and retail-to provide proof of concept. The findings indicated gains in accuracy, the elimination of latency, and cost reduction resulting from the reuse of a pipeline and orchestration efficiency. Metrics such as fraud recall, false favourable rates, and execution throughput were calculated and compared to those of their pre-existing, siloed systems. It also reflects on some potential bottlenecks, including schema drift management, microservices dependency management, and integration overheads with vendor platforms. This paper provides a comprehensive view on establishing universal data engineering frameworks applicable for fraud detection tasks, offering insights into modular pipeline design, cloud deployment architectures, and domain-specific customizations. Through the lens of data interoperability, pipeline observability, and real-time reaction responses, the research provides an architecture roadmap for constructing enterprise-grade fraud detection environments that can adapt to modern digital infrastructures.
Connecting the Dots: Exploring the Fundamental Underpinnings of Deep Learning
Deep learning has transformed various sectors, introducing new applications and opportunities. However, the underlying physical mechanisms or mathematical theories responsible for its success remain fundamental questions. This inquiry explores the connection between deep learning algorithms and established scientific principles with the aim of uncovering the mysteries behind their remarkable capabilities. By bridging the gap between deep learning, neural networks, and scientific knowledge, we can develop robust and interpretable models with enhanced capabilities. This ongoing research involves collaboration across diverse fields to unveil the hidden intricacies of deep learning algorithms and their links to physical phenomena. The ultimate goal is to contribute to the potential of the journal by examining the theory, design and application of neural networks and machine learning, focusing on the effectiveness of neural network paradigms for deep learning and their connections to physical events. By examining the intersection of deep learning, neural networks, and physical phenomena, we aim to advance our understanding and use of neural networks and machine learning in many areas of space, pushing the boundaries of excellence in science and engineering
Deep Learning for Medical Image Analysis: Advances, Challengesand Future Prospects
With the rise of deep learning, medical image analysis has undergone fundamental change, presenting new possibilities to physicians in terms of achieving higher accuracy more efficiently, and greater automation in disease diagnosis and prognosis. In this paper, we discuss recent progress in utilizing deep learning techniques such as convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), and transformer-based model for medical image analysis. Image classification, segmentation, and anomaly detection in modalities such MRI, CT, Xray, and ultrasound have been strongly improved by these architectures. Yet, such progress brings challenges, namely scarcity of sufficient data, interpretability of models, computational intensity, and robust generalization to diverse clinical datasets. At the same time regulatory concerns and ethical questions on the use of AI diagnostics are still constraining large scale adoption. This paper provides a comprehensive review of state-of-the art deep learning models in medical imaging, reviews the current limitations, and envisions future research directions in federated learning, explainable AI, and quantum enhanced deep learning. Such insights are intended to guide researchers and clinicians in the optimization of AI driven medical image analysis for optimized patient care and clinical decision making.
Zero-Knowledge Infrastructure Verification: A Comprehensive Guide to Chaos SecOps Implementation
This paper introduces a novel framework for Zero-Knowledge Infrastructure Verification (ZKIV) that combines chaos engineering principles with security operations and zero-knowledge proofs to create a robust infrastructure verification system. By leveraging these technologies within a DevOps context,organizations can validate the integrity and security posture of their infrastructure without revealing sensitive configuration details or credentials. This approach, which we term Chaos SecOps, represents a significant advancement in infrastructure security verification, enabling teams to verify compliance,detect misconfigurations, and identify vulnerabilities without exposing sensitive information. Through a detailed AWS implementation case study, this paper demonstrates how ZKIV can be applied to modern cloud environments to enhance security, streamline compliance verification, and build resilient systems. Executive Summary This paper introduces Zero-Knowledge Infrastructure Verification (ZKIV), a novel framework for validating the security and compliance of complex,modern infrastructure (particularly cloud environments like AWS) without exposing sensitive configuration details or credentials. ZKIV achieves this by combining principles from: • Zero-Knowledge Proofs (ZKPs): While full cryptographic ZKPs are discussed, the paper focuses on "functional zero-knowledge" approaches practical for infrastructure. This means proving that security controls are in place and functioning correctly without revealing the underlying configurations themselves. Examples include black-box testing, output-only verification, and attestation. • Chaos Engineering: The deliberate introduction of controlled failures (like misconfigurations or simulated attacks) to test system resilience and the effectiveness of security controls. • Security Operations (SecOps): Continuous monitoring, threat response, and security automation practices. • DevOps: Leveraging automation, continuous integration/continuous delivery (CI/CD), and Infrastructure as Code (IaC). The integration of these disciplines is termed ChaosSecOps. Key Benefits of ZKIV • Enhanced Security: Verification happens without needing to expose sensitive data, reducing the attack surface. • Improved Compliance: Continuous, automated verification ensures ongoing adherence to regulatory and internal security policies (e.g., PCI DSS,SOC 2). Evidence is collected in a zero-knowledge manner. • Reduced Operation Risk: Proactive identification of vulnerabilities and misconfigurations before they can be exploited. • Increased Confidence: Greater assurance in the security posture due to systematic and continuous testing. • Scalability: Verification is automated and can be used across many systems. • Efficiency: Verification can be done faster. ZKIV Framework Components The framework consists of several key components that work together: • Verification Orchestrator: The central control point for scheduling, executing, and managing verification tests. • Policy Engine: Defines and enforces security and compliance rules (using policy-as-code). • Test Agents: Ephemeral (short-lived) components deployed within the infrastructure to perform black-box testing. • Evidence Collection System: Gathers test results in a way that preserves zero-knowledge (no sensitive data revealed). • Remediation Framework: Automates the fixing of identified security issues. AWS Implementation Case Study A detailed case study demonstrates ZKIV implementation within a financial services organization using AWS. Key AWS services used include AWS Organizations, Security Hub, Lambda, Step Functions, EventBridge, Systems Manager, S3, and Config. The case study shows practical application of zero knowledge techniques like: • Least-Privilege IAM Roles: Verification agents have only the permissions needed to check configurations, not to access the data they protect. • Output-Only Verification: Validating database security settings without querying the database itself. • Black-Box Network Testing: Using isolated containers to test network segmentation without accessing internal network configurations. Measurable Results (From The Case Study) • 65% reduction in compliance audit preparation time. • 87% decrease in security incidents related to misconfigurations. • Verification scaled from 50 to 5,000 infrastructure components in one year. Future Directions The paper outlines several emerging technologies that will shape ZKIV evolution: • Cryptographic zero-knowledge proofs for efficient verification • AI-enhanced capabilities for automated test generation and predictive analysis • Immutable infrastructure verification with supply chain integrity As infrastructure environments grow in complexity, ZKIV provides organizations with a methodology for maintaining security and compliance at scale,enabling them to build and operate resilient systems with confidence.
Traditional infrastructure verification methods
Zero-Knowledge Infrastructure Verification (ZKIV)
Intelligent Citrus Classification: An Automated System Using Weight Sensing and Image Processing with Arduino Microcontroller
The citrus industry is a major player in the agricultural sector, and the preservation of citrus fruit quality and marketability depends on effective citrus fruit classification and sorting. The objective of this research was to create and assess an automated system for classifying and organizing citrus fruits according to their color, roundness, weight, and size. The system was developed with Arduino microcontrollers and image processing software to examine and classify fruits into discrete categories: extra small, small, medium, large, and extra-large. The evaluation of the automated system's performance included a comparison between its categorization outcomes and those obtained via manual approaches. The statistical study showed that there were no significant disparities between the average values acquired manually and those obtained by automation. This suggests that the automated system is capable of achieving the same level of accuracy as human classifiers. The system exhibited a notable level of uniformity in categorizing fruits according to the given characteristics, with average values for weight, diameter, roundness, and color % displaying a robust association with the predetermined size categories. The findings demonstrate that the automated system has the ability to precisely categorize citrus fruits with a significant level of uniformity, providing a practical solution to improve the effectiveness and excellence of fruit processing in the citrus sector. Implementing automated classification and sorting systems in the citrus industry has the capacity to greatly improve efficiency, leading to increased profitability and a more sustainable agricultural approach. Ultimately, this research effectively created and evaluated an automated system for classifying and sorting citrus fruits, which showed promising outcomes in terms of precision and uniformity. The deployment of this system provides a practical solution for improving the productivity and standard of fruit processing in the citrus sector, hence promoting a more lucrative and environmentally friendly agricultural approach.
The Power of APIs in the Managed File Transfer (MFT) World Enhancing Automation, Security and Scalability
Modern businesses rely on secure and efficient file transfer mechanisms to support critical operations. Managed File Transfer (MFT) solutions have evolved significantly with the integration of Application Programming Interfaces (APIs), enabling automation, enhanced security, and improved scalability. APIs streamline file exchange processes, reduce manual interventions, and ensure compliance with modern security protocols. As organizations continue to adopt cloud-based and hybrid infrastructures, API-driven MFT is becoming essential for seamless data movement across systems. This paper will explore how APIs are transforming the MFT landscape by enhancing automation, security, and scalability. We propose that API-driven MFT solutions offer a future-proof approach to secure and efficient file transfers, addressing evolving enterprise needs.
The Evolution of AI Agents: From Rule‐Based Systems to Autonomous Intelligence – A Comprehensive Review
Artificial Intelligence (AI) agents have evolved from early rule‐based systems to today’s sophisticated autonomous systems. This comprehensive review examines the historical development, technical advancements, and emerging trends in AI agent research. Specifically, we address the following research questions: • How have AI agent architectures evolved over time, and what factors drove these changes? • What are the strengths and limitations of rule‐based versus learning‐based approaches in real-world applications? • How can ethical frameworks and empirical case studies inform future developments in AI agent technology? This article outlines the selection criteria for the literature review, presents empirical examples from diverse application domains, and critically analyzes methodologies. It discusses both achievements and persistent challenges in AI agent research while offering recommendations for future research directions and governance.
Federated Reinforcement Learning for Edge AI Decision-Making in 6G-Enabled V2x Systems
The evolution toward sixth‑generation (6G) networks introduces transformative capabilities in intelligent transportation, particularly throughultra‑reliable, low‑latency vehicle‑to‑everything (V2X) communication. As autonomous and connected vehicles generate vast amounts of data at the edge, conventional centralized learning approaches are increasingly constrained by privacy, bandwidth, and latency limitations. In this paper, we present a federated reinforcement learning (FRL) framework that enables distributed edge agents—such as vehicles and roadside units—to collaboratively learn real‑time decision policies for navigation, collision avoidance, and traffic optimization, without sharing raw data. Our approach models the V2X environment as a decentralized multi‑agent Markov decision process (MDP) and introduces an adaptive aggregation mechanism that accounts for node mobility and communication variability. We implement and evaluate the framework using a co‑simulation environment that integrates SUMO for traffic dynamics and ns‑3 for network emulation. Experimental results demonstrate that our FRL method outperforms centralized baselines by reducing average decision latency by 32 percent, while preserving data privacy and achieving robust convergence under intermittent connectivity. This work advances the deployment of edge AI in future vehicular ecosystems, providing a scalable, privacy‑preserving foundation for real‑time intelligence in 6G‑enabled V2X systems.
6G, Vehicular Networks, V2X, Federated Reinforcement Learning, Edge Intelligence, Ultra‑Reliable Low‑Latency Communications, SUMO, NS‑3
AI Innovations in Cloud Financial Operations: A Framework for the Future
This dissertation critically examines the integration of artificial intelligence (AI) within cloud financial operations (FinOps) to enhance both the efficiency and adaptability of financial management. Specifically, it addresses the critical concern of inadequacies in current FinOps strategies, which often fail to effectively predict and mitigate financial risks associated with fluctuations in cloud investments. The study employs a systematic analysis of historical financial data, operational performance metrics, and AI predictive models to uncover robust strategies for future-proofing cloud FinOps. Key findings indicate that leveraging AI not only improves forecasting accuracy and risk assessment but also empowers organizations to optimize cloud expenditures while ensuring financial stability in the face of dynamic market conditions. The importance of these findings lies in their capacity to fundamentally transform financial management practices. By adopting AI-enhanced FinOps strategies, organizations can mitigate financial risks and foster sustainable financial practices that promote continuous innovation and efficiency in service provision. Furthermore, the broader implications of this research extend to shaping policy decisions and refining operational frameworks. These advancements ultimately aim to support more resilient financial management systems that are better equipped to navigate the rapidly evolving landscape of cloud technology and its accompanying financial complexities, prompting deeper inquiry into the efficacy and application of AI in diverse organizational contexts.
Cloud, FinOps, Artificial Intelligence, AI-Adaption
Enhanced Machine Learning Algorithm to Predict Lung Cancer
Lung cancer ranks among the top causes of cancer-related fatalities globally and is frequently detected at later stages when therapeutic options are scarce.Timely diagnosis is vital for enhancing the survival rates of patients. This paper examines the use of the Support Vector Machine (SVM) algorithm for lung cancer prediction. SVM is a supervised machine learning method known for its efficacy in classification tasks, particularly in high-dimensional contexts. In this research, the dataset is evaluated using various attributes, including age, gender, smoking background, and imaging results, to train the SVM model.The study highlights the promise of machine learning methods, particularly SVM, in aiding healthcare professionals with early detection and improving patient outcomes.
Optimizing LLM Inference: Metrics that Matter for Real Time Applications
The deployment of Large Language Models (LLMs) including GPT, LLaMA, Claude, and Gemini occurs in real-time applications which require both lowlatency and high-throughput inference. The transition of these models from research to production systems requires essential evaluation of their inference performance. This paper provides a detailed analysis of six performance metrics which are commonly used to evaluate LLM inference: Time to First Token (TTFT), Generation Time, End-to-End Latency (e2e_latency), Inter Token Latency (ITL), Tokens Per Second (TPS), and Requests Per Second (RPS). This paper examines the definitions and practical implications and interrelationships between these metrics through descriptive analysis and empirical observations. Our research demonstrates how responsiveness and throughput create trade-offs while showing that applications need specific metrics for optimization. The research provides practical guidance to researchers and engineers and system architects who want to evaluate or enhance LLM systems in latency-critical and shared infrastructure environments.
Large Language Models (LLMs), Time to First Token (TTFT), End-to-End Latency, Inter Token Latency (ITL), Tokens Per Second (TPS), Requests Per Second (RPS), Inference Benchmarking, Latency Optimization
Generative Artificial Intelligence Unveiled
This journal discusses key concepts of generative AI by utilizing the Large Language Models application architecture. Briefs about the architecture of Generative AI, capabilities of Generative AI and prominence of LLMs across various domains. The paper also discusses some of the applications and implications of generative AI.
Border Boundaries: Navigating Migration and the Role of AI in Irregular Movements
This study examines the impact of AI-driven technologies in migration management, focusing on surveillance, biometric identification, and predictive analytics at international borders. While these technologies enhance border security and streamline administrative processes, they also raise concerns about bias, data privacy, and ethical risks. In fact, if flawed, automated decision-making may lead to wrongful rejections and human rights violations. A key challenge is balancing technological efficiency with human rights protections as AI-driven border control systems, if unregulated, can result in discriminatory outcomes and erode fundamental freedoms. Issues such as opaque decision-making, mass surveillance, and potential data misuse highlight the need for transparency and accountability in AI deployment. As governments and institutions are being challenged in ensuring that AI in migration governance aligns with legal and ethical standards, international frameworks, including human rights treaties, emphasize the necessity of safeguards to prevent excessive surveillance and uphold migrants’ rights. Therefore, AI use is rights-based if enhancing security without compromising due process, privacy, or fairness. This study evaluates the benefits and risks of AI in border control, advocating for governance frameworks that integrate oversight, transparency, and ethical considerations. By aligning AI with humanitarian principles, migration policies can be both effective and just.
Generative AI for Edge-Based Predictive Maintenance in Smart Factories
In this article, we introduce a lightweight AI system named GenAI, predestined for industrial IoT devices to enable proactive predictive maintenance at the edge. The proposed technique for monitoring and generating synthetic sensor data tries to rebuild the meaningful signal variation in the small common datasets and transfer it into sensor signals that represent the actual data variability regarding equipment operation. Reducing downtime to avoid maintenance requirements is especially critical in advanced factory settings, where equipment failures are too costly. Furthermore, the local processing of generative bidding at the edge does not need to be connected to the main network, meaning that data must be protected from neighbor interference. Through our new platform strategy, the apparent issue of the acute underdevelopment of local data within a distributed and hugely scalable system is explained in order to prove its applicability within big industrial applications.
API Integration and Modernization in FinTech: Effective Strategies and Optimal Solutions
The Financial Technology (FinTech) sector thrives mainly because of advancements in digital currencies and artificial intelligence. These developments reshape the fraud detection and risk management approach while improving personalized financial services within strict regulatory frameworks. Financial institutions face security vulnerabilities, compatibility issues with legacy systems, and a landscape of constantly changing regulations. Organizations must address these security risks, compatibility challenges, and the evolving nature of regulatory requirements. To overcome these obstacles, organizations must thoroughly evaluate their current systems, enhance security protocols, and focus on cost-effectiveness during transitions. Application Programming Interfaces (APIs) have become vital as the financial industry becomes more electronic and unified. They permit the secure data exchange and empower seamless integration across various platforms. While some institutions still depend on in-house solutions, many choose scalable, commercially available APIs to reduce operational burdens and improve efficiency. However, integrating or updating APIs into existing financial infrastructure can be challenging.
AI Tutors in E-Learning: Analyzing Personalized Learning Pathways
The integration of artificial intelligence (AI) in e- learning has ushered in a transformative era, enabling personalized learning pathways tailored to individual student needs. This research investigates the impact of AI-powered personalized tutors on student engagement and learning outcomes. By synthesizing insights from existing literature and conducting an empirical evaluation, this study demonstrates how AI systems dynamically adapt learning experiences, resulting in improved engagement and retention. However, challenges such as data privacy, algorithmic bias, and the ethical implications of automated learning systems require attention. This paper highlights the need for robust frameworks to ensure equitable, transparent, and effective deployment in diverse educational contexts. The findings provide actionable insights for educators, policymakers, and developers aiming to maximize the benefits of personalized AI in e-learning.
Developing Software for Automated Firmware Updates in Fuel Controllers
The rapidly evolving landscape of fuel station management necessitates a robust and secure firmware update mechanism for fuel controllers to maintain operational efficiency, regulatory compliance, and cybersecurity integrity. Traditional manual update methods introduce latency, increase human errors, and expose vulnerabilities that can compromise overall system reliability. This paper presents a comprehensive software-driven framework to automate firmware updates for fuel controllers using cloud-based infrastructure, significantly reducing the burden of manual intervention. By leveraging AWS S3 as a secure, scalable storage solution for firmware versions and AWS IoT to facilitate real-time update notifications, the proposed system ensures streamlined and reliable firmware distribution. The architecture is designed to minimize downtime by intelligently scheduling updates, securing transmission channels through advanced encryption protocols, and enabling remote management capabilities for widespread fuel station networks. Additionally, the system integrates authentication and version control to maintain consistency across distributed deployments. Through case studies and performance evaluations, we demonstrate the effectiveness of this automated approach in enhancing scalability, ensuring update integrity, and improving overall system resilience.