Global Journal of Computer Science and Technology
Global Journal of Computer Science & Technology We are one of the world's leading and fastest-growing research publications with the paramount objective of discovering advances by publishing insightful, double-blind, peer-reviewed scientific journals.
Blending Automation and Human Expertise in SRE for Banking Applications
The banking sector presents unique challenges for Site Reliability Engineering practices due to stringent regulatory requirements complex technical environments and zero-tolerance for financial data errors This article explores the critical balance between automation capabilities and human expertise in maintaining reliable banking applications It examines the distinctive reliability requirements of financial systems identifies effective automation strategies within regulatory constraints articulates the irreplaceable components of human judgment and proposes an implementation framework for optimal collaboration between automated systems and human operators Through detailed analysis of banking-specific failure modes monitoring approaches and incident response workflows the article provides a structured approach to developing SRE practices that leverage both technological capabilities and human cognitive strengths while respecting the unique constraints of financial environments The framework presented enables banking institutions to implement reliability practices that maintain transaction integrity meet regulatory obligations and support business objectives through carefully designed human-automation systems
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Adaptive Stream Processing with Reinforcement Learning: Optimizing Real-Time Data Pipelines
This article explores the integration of Reinforcement Learning RL with stream processing systems to address the fundamental challenges of handling unpredictable workloads and dynamic resource constraints Traditional stream processing frameworks rely on static configurations that struggle to adapt to fluctuating conditions leading to either resource over provisioning or performance degradation The article presents RL as a promising solution through intelligent agents that continuously learn from system performance to optimize crucial parameters including task scheduling resource allocation checkpoint frequency and load balancing It examines the critical importance of adaptivity in stream processing outlines RL fundamentals applicable to this domain and details specific applications including dynamic resource allocation task scheduling optimization adaptive check pointing and intelligent load balancing Additionally it addresses implementation challenges such as training overhead reward function design cold start problems and integration with existing frameworks Current tools and frameworks enabling RL-enhanced stream processing are evaluated and future research directions including multi-agent RL federated reinforcement learning explainable RL for operations and green computing optimization are discussed
Single Point of Integration to Enterprise Resource Planning Systems
Enterprise Resource Planning systems form the basis of infrastructure for modern business operations controlling key organizational activities across financial human resource supply chain and customer relationship areas The surge in specialized business applications brings about ever more challenging integration environments where traditional point-to-point connectivity models create overwhelming operational challenges such as architectural fragmentation high maintenance costs and data integrity degradation Single Point Integration methodology presents itself as an innovative solution creating centralized integration layers via middleware platforms API gateways and enterprise service bus technologies The centralized architectural paradigm avoids duplicated integration logic normalizes data transformation processes and enacts unified security protocols for all external system integrations Implementation effects display a good-sized operational performance improvement with decreased improvement timeframes more desirable statistics consistency and simplified protection tactics Strategic business blessings include more organizational flexibility eased regulatory compliance and quicker digital transformation talents Service-oriented architecture principles inform centralized integration solution lifecycle management whereas event-driven frameworks create durable technical foundations for managing intricate system interaction and real-time communication demands Cost-benefit analysis demonstrates considerable return on investment through lower implementation complexity and enhanced use of resources The design transformation towards centralized integration is a fundamental move away from multifragmented connectivity patterns towards enduring scalable enterprise integration approaches that facilitate long-term business growth and competitive advantage in adaptive market spaces
Building the Digital IT Service Desk: How Generative and Agentic AI Are Powering Employee Self-Service
The digital transformation of IT service desks represents a paradigm shift away from traditional ticketing systems toward intelligent autonomous support platforms powered by Generative and Agentic AI technologies This evolution addresses fundamental challenges in employee technology support by providing immediate personalized solutions without the friction of conventional support channels The architecture of these advanced systems integrates conversational interfaces with knowledge management repositories and autonomous decision-making capabilities enabling sophisticated reasoning across complex enterprise environments Generative AI revolutionizes how technical information is communicated through dynamic content creation tailored to individual contexts and skill levels while maintaining conversation continuity across extended interactions Agentic components transform passive support into proactive solutions through autonomous task execution intelligent request routing and continuous learning mechanisms that adapt to organizational changes Implementation success depends on strategic phased deployment with robust change management practices while maintaining enterprise-grade security and regulatory compliance through comprehensive protection frameworks and regular validation processes
Ethical Frameworks for AI-Driven Decision Systems: A Comprehensive Analysis
This article presents a comprehensive analysis of ethical frameworks for artificial intelligence-driven decision systems addressing the critical need for responsible AI deployment across industries As AI systems increasingly influence high-stakes decisions in healthcare finance criminal justice and other sectors the imperative for robust ethical guidelines becomes paramount The article examines four core ethical principles-fairness transparency privacy and accountability-that form the foundation of responsible AI development exploring their interconnected nature and practical implementation challenges Through analysis of implementation strategies and best practices the article demonstrates how organizations can translate abstract ethical principles into operational reality through systematic approaches including ethical impact assessments technical implementation of fairness-aware algorithms organizational governance structures and meaningful stakeholder engagement Industry-specific case studies from healthcare financial services criminal justice and autonomous vehicles illuminate both successful implementations and cautionary tales revealing how ethical considerations vary across sectors based on decision contexts stakeholder impacts and regulatory environments The article further examines the rapidly evolving regulatory landscape comparing divergent approaches across jurisdictions including the European Union s comprehensive risk-based framework the United States fragmented sector-specific regulations and emerging international standards By synthesizing insights from computer science philosophy law and social sciences this analysis provides practitioners with actionable guidance for developing AI systems that balance technological innovation with human values ensuring that AI advancement enhances rather than diminishes human dignity autonomy and societal well-being
Unifying Machine Learning and Physics Models Through a Mesoscopic Field Approach
We present a path-integral methods field solution that merges machine learning with microscopic physics models for mesoscopic phenomena This interpretable multiscale algorithm treats physical and machine learning field solutions as equivalent enabling seamless integration of microscopic physics intomachine learning algorithms for mesoscopic pattern learning and generation Our approach incorporates microscopic physics mechanisms as hidden fields and represents their interactions with mesoscopic fields through auxiliary fields Rather than imposing statistical assumptions on hidden nodes and learning weight statistics from data our method derives a hidden fields formalism based on physics interaction mechanisms and determines connecting weights through action functional minimization and neural operators machine learning Combining the strengths of both physicsmodeling and machine learning techniques our method achieves strong performance in learning and generating mesoscopic patterns from limited data It can capture physics interactions occurring at different scales allowing forextrapolation when dealing with patterns with different interacting parameters and pattern evolution dynamics We demonstrate our solution through a concrete case of two interacting species with microscopic chain structures widely used for polymer material and biomolecular simulation Our mesoscopicfield approach unifying machine learning and physics modelscan be readily usedin various areas of material science biology and social dynamics
AI-Driven Automated Compliance Monitoring in SAP & Salesforce: A Comprehensive Technical Implementation Guide
Regulatory compliance issues present within the organizations are intensifying in the frameworks of SOX GxP FDA 21 CFR Part 11 and ISO 13485 because manual monitoring is not suitable in the contemporary environment of enterprise transaction volumes The implementation guide is a technical outline of automated tools for compliance monitoring by AI in an integrated Sales force and SAP environment This framework consists of real-time transaction analysis engines automatic generation of audit trails end-to-end traceability modules and GRC integration layers that process a stream of continuous data and apply advanced anomaly detection algorithms Implementations that are specific to SAP make use of SOX compliance automated through the AI Core platform continuous auditing technology and GxP quality management integration The Sales force solutions employ the Einstein AI platform security capability and Shield overall monitoring with revenue recognition controls and privacy issue management The integration of cross-platforms using IBM Open Pages and enterprise immune system allows the coherent checking of compliance using behavioral analytics and automated testing frameworks The results of the implementation show that compliance effectiveness has been increased significantly on the basis of predictive risk assessment and proactive prevention of violations
Understanding Backend for Frontend Architecture: Exploring Backend for Frontend (BFF) Architecture Across Platforms and its Dynamic Adaptations
The Backend for Frontend BFF architecture exemplifies a paradigm shift in modern software development practices addressing the multifaceted complexities of deployings applications across multiple platforms and digital environments This comprehensive technical review introduces the principles and practical usage of BFF architecture illustrating how platform-specific backend services streamline data processing and data management for a unique set of frontend platforms including mobile applications websites IoT devices and emerging digital touch points
State Management in Large-Scale Enterprise Frontends: Choosing Between NgRx, Redux, and Pinia
This article examines the evolution of state management solutions in large-scale enterprise frontend applications focusing on NgRx Angular Redux React and Pinia Vue Beginning with the conceptual shift from MVC to Flux Redux patterns the article explores how each framework-specific implementation addresses complex state challenges through different architectural approaches NgRx leverages RxJS to create a reactive state system aligned with Angular s design philosophy while Redux has evolved through Redux Toolkit to improve developer experience while maintaining its core principles Pinia represents Vue s modern approach emphasizing simplicity and developer experience without sacrificing power Through comparative analysis of architectural patterns performance characteristics and developer experience metrics the article provides decision frameworks for enterprise architects selecting appropriate state management solutions based on framework alignment application complexity and team dynamics The analysis extends beyond theoretical comparisons to include practical implementation considerations identifying key trade-offs in complexity versus flexibility initial development speed versus long-term maintainability and framework-specific optimizations that impact both runtime performance and developer productivity across the application lifecycle from initial architecture to ongoing maintenance and feature expansion
From Cybersecurity to Cyber Resilience: A Paradigm Shift Toward Organization-Wide Adaptive Defense
Organizations are increasingly facing increasingly advanced cyber threats for which traditional security frameworks are struggling to cope The typical cybersecurity framework that thousands of organizations have adhered to which is centric to technical controls and departmental silos is ultimately inadequate for maintaining business operations during and after cyber incidents Cyber resilience as a newly evolving potentially revolutionary model extends beyond the protective framework to encompass anticipating the dynamic threat landscape enduring capabilities to build organizational strength and capacity to withstand events recovery iterative remediation and drawdown timelines and evolving capabilities to adapt to and change as result of exposures incidents and or events This framework represents the cybersecurity threat as part of a broader perspective on business resilience requiring transformation at the organization level and culture rather than narrowly focused technical fixes Shifting the focus from reactive protection to proactive resilience necessitates a cross-functional approach that focuses as much on the technical stack as it does the organizational environment by breaking through barriers of security teams and operations functions that are historically siloed Cybersecurity prioritizes attack prevention through narrowly defined procedures and protective technologies while cyber resilience needs to prioritize maintaining minimum business functions in the event of potential adversity Framework has four pillars - anticipate withstand recover and evolve that describe all-encompassing guidance of organizational requirements of organizational capacity and sustainable defense The implementation of cyber resilience will necessitate organizational culture change away from the responsibility of security being a technical accountability and ultimately transforming security to organizational accountability The transitional shift is an elegant evolution as a methodology to build adaptive capacity and eliminate risk tolerance rather than a model of mitigating risk exposure This places a condition on which organizations need to operate to flourish in the quasi-daily presence of cyber threats while fulfilling operational efficacy and business continuity globally
Secure Cross-Region Service Communication Using AWS EC2 PrivateLink in a Zero Trust Framework
This article explores the implementation of Zero Trust security principles in cross-region AWS architectures using EC2 Private Link As organizations expand globally maintaining security across distributed environments becomes increasingly complex The article examines three architectural patterns- Hub-and-Spoke Mesh Network and Regional Isolation- evaluating their effectiveness for secure service-to-service communication across AWS regions The article analysis with traditional approaches such as VPC Peering and Transit Gateway reveals significant advantages of Private Link-based architectures in terms of security posture operational efficiency and compliance capabilities The article addresses critical operational considerations including monitoring latency optimization data sovereignty compliance and cost management Through case study of implementation in a global financial services environment the article demonstrates substantial improvements in security performance and compliance outcomes The article concludes with emerging AWS capabilities and promising research directions for next-generation Zero Trust architectures
How a Global Retail Bank Transformed Decision-Making with Secure AI Analytics
The way businesses manage data architecture and security systems has evolved significantly as a result of the broad use of artificial intelligence technology in commercial settings The substantial security implications of federated data architectures over centralized ones in AI-augmented environments are examined in this study with particular attention paid to the complex interrelationships between data sovereignty access control mechanisms encryption techniques and regulatory compliance requirements Federated architectures demonstrate improved capabilities to maintain data locally and support collaborative AI techniques through privacy-preserving techniques particularly supporting enterprises operating across multiple jurisdictions with stringent data localization requirements The decentralized aspect of federated systems delivers built-in resilience against security violations by reducing exposure range while enhancing real-time threat identification and response abilities Centralized systems provide benefits in cohesive governance complete audit logs and easier compliance oversight but they also create concentrated risk areas and possible issues with data sovereignty regulations Identity and access management systems display unique traits in both paradigms where centralized models ensure uniform policy enforcement while federated methods facilitate cross-domain authentication via advanced trust connections Implementations of encryption protocols differ markedly across architectures as federated environments necessitate sophisticated cryptographic methods such as secure multi-party computation and homomorphic encryption to maintain privacy in collaborative analytics Regulatory compliance frameworks like GDPR and HIPAA exhibit differing connections with architectural decisions as federated models inherently adhere to data localization demands whereas centralized systems enhance thorough compliance oversight The advancement of privacy-enhancing technologies keeps linking architectural paradigms facilitating hybrid methods that merge the governance benefits of centralization with the sovereignty perks of federation
Event-Driven Microservices for Ultra-Low Latency Cloud Workflows
Modern cloud-native applications require new-age architectural paradigms that can provide instantaneous responsiveness in handling heterogeneous data streams over distributed computing environments Event-driven microservices architectures come into play as groundbreaking solutions to counter the inherent constraints of monolithic systems and traditional batch-based processing pipelines The architectural system brings together containerized microservices and advanced event streaming infrastructure to support asynchronous communication patterns that do away with legacy blocking operations Machine learning algorithms enable smart event prioritization and predictive resource allocation dynamically adjusting to changing workloads with adaptive scaling options Multi-cloud deployment strategies guarantee outstanding fault tolerance with full self-healing options and geographical redundancy deployments Performance optimization approaches involve connection pooling in-memory caching and streaming computation models that cut end-to-end processing latency by a significant margin Horizontal scaling support allows dynamic capacity options with constant latency characteristics despite changing operational loads Applications within the real world cover industrial automation medical monitoring smart town infrastructure financial services autonomous transportation and supply chain management displaying tremendous upgrades in machine responsiveness useful resource usage performance and operational reliability over traditional architectural styles
Copyright (c) 2025 Global Journal of Computer Science and Technology
Blockchain-Based Identity Management for Enterprise Cloud Authentication
Cloud computing has greatly changed how businesses work bringing better scaling and efficiency At the same time this shift has created some security problems in how organizations handle identity and access which is key to keeping businesses secure Old-fashioned identity systems are easy to manage because they let users sign in once to access everything But this setup has a single point of failure making it a target for attackers More and more cyberattacks are aimed at these central identity systems showing problems in how organizations verify identities Identity-related security issues are costing companies money and disrupting their work Blockchain along with the idea of self-sovereign identity gives a good option instead of the usual central systems Decentralized identity systems use identifiers and credentials based on blockchain This gets rid of single points of failure while improving user privacy and control over data This way to check trust is stronger resists attacks and system errors and still follows rules about protecting privacy
Signal-Driven Decision Systems in Enterprise Cloud Platforms: A Data-Informed Approach to Platform Optimization
As more businesses adopt distributed architectures that require complex optimization techniques enterprise cloud platforms encounter previously unanticipated challenges in maintaining optimal performance security and cost-effectiveness By employing real-time telemetry data sophisticated machine learning methods and responsive feedback loops to develop self-optimizing cloud operations signal-driven decision systems represent a revolutionary approach Signal classification taxonomies that distinguish performance measurements resource usage indications security issues and user behavior patterns across time scales and data sources are included in the full framework Algorithmic scoring models incorporate statistical analysis ensemble methods and security-aware normalization techniques to transform raw signal data into actionable optimization recommendations while maintaining multi-tenant isolation requirements Control system architectures apply proportional-integral-derivative principles and adaptive feedback loops operating at multiple organizational levels from immediate operational responses to strategic platform evolution decisions The integration of security frameworks and operational maturity modeling enables ongoing monitoring prompt threat identification and automated incident management Implementation strategies concentrate on techniques for a phased rollout that minimize operational disruptions and improve conformance with existing infrastructure It is anticipated that contemporary technologies such as ensemble-based deep learning techniques edge computing and quantum processing would significantly improve signal processing capabilities and optimization accuracy Businesses approaches to cloud governance and optimization are being drastically altered by unprecedented levels of automation and intelligence in cloud platform management which are made possible by the integration of artificial intelligence stream processing and predictive analytics technologies
API-Led Connectivity for SAP S/4HANA: Unlocking Business Value through Reusable Services
Organizations face mounting pressures to extract maximum value from SAP S 4HANA investments while maintaining market flexibility This article presents API-led connectivity as a strategic catalyst transforming enterprise integration through layered architecture-system process and experience APIs-where business functions become reusable services System APIs reveal critical company information while process APIs manage intricate business tasks and experience APIs shape output for different user needs Keeping these connections secure requires strict access rules layered safeguards and proper data handling practices Effective governance tools help companies monitor usage and scale connections across business partners Factory operations flow more efficiently retail stores connect online and in-person shopping effortlessly and financial institutions bring new capabilities to market with less delay creating competitive edges reducing expenses and building stronger customer relationships Companies can respond to changing business conditions without endangering essential operations elevating S 4HANA beyond basic accounting into a platform that drives business advancement and fresh opportunities
Building a Scalable UVM-Based Testbench for GPU Compute Units
Modern graphics processing units have evolved into complex massively parallel computing engines that demand sophisticated verification methodologies capable of validating thousands of concurrent threads executing across intricate memory hierarchies and specialized execution pipelines Traditional verification approaches struggle to adequately address the unique challenges posed by Single Instruction Multiple Thread execution models dynamic thread scheduling and complex interactions between compute units and multi-level cache systems This article presents a comprehensive Universal Verification Methodology-based testbench architecture specifically designed for GPU compute unit verification addressing critical gaps in existing verification practices through innovative SIMT-aware stimulus generation integrated memory subsystem modeling and scalable test generation frameworks The proposed framework combines established UVM principles with GPU-specific verification techniques creating a modular and reusable architecture that supports diverse configurations while maintaining systematic coverage collection and intelligent corner case detection Extensive experimental evaluation across representative GPU workloads demonstrates substantial improvements in verification quality debug efficiency and development productivity compared to traditional approaches The architecture s parameterized design enables seamless adaptation across different GPU generations while its extensible structure provides a foundation for future verification challenges including AI accelerators and chiplet-based architectures
universal verification methodology (UVM)
Copyright (c) 1970 Global Journal of Computer Science and Technology
Silent Data Errors in GPUs: Challenges and Mitigation in Modern Silicon
Silent data errors in graphics processing units SDEs represent a critical challenge for modern computational systems that rely on these accelerators in high-performance computing artificial intelligence and data center operations These errors propagate through calculations without triggering detection mechanisms potentially compromising results in critical applications from autonomous vehicles to medical diagnosis Quantitative analysis reveals disturbing error rates 8 15 10 -3 FIT per device at sea level one error per 14 000 device-hours with error rates increasing 17-32 when running at full computational capacity in data centers The physical causes of SDEs include cosmic radiation causing 61 7 of faults to propagate undetected in streaming multiprocessors manufacturing variations contributing to 4 3 of silent computational failures thermal stress cycles voltage fluctuations and aging effects that impact semiconductor reliability Architectural vulnerability varies significantly register files exhibit 36 silent data corruption rates versus 23 for shared memory and 11 for global memory while instruction vulnerability ranges from 6 1 for integer operations to 42 7 for atomic operations Workload characteristics dramatically affect error sensitivity with machine learning inference showing up to 19 3 accuracy reduction from moderate error rates in transformer models versus 8 6 in convolutional networks Mitigation strategies span hardware ECC reducing corruption by 78 5 firmware and software domains with recent selective redundancy techniques achieving 91 error coverage with only 32 performance overhead Cross-layer resilience approaches demonstrated in recent research can reduce critical data integrity errors by up to 93 4 compared to default protection methods Understanding these complex interactions and implementing targeted protection systems is essential for developing resilient GPU computing platforms that maintain both performance at scale and reliability
SAP S/4HANA Universal Journal: The Digital Bridge Between Finance and Logistics Operations
This article examines the transformative integration capabilities of SAP S 4HANA Finance Cloud with logistics modules highlighting the architectural foundations and business value of unified financial-operational data management The article explores how S 4HANA s Universal Journal ACDOCA creates a single source of truth that eliminates traditional boundaries between finance and logistics domains enabling real-time transaction processing and eliminating reconciliation requirements The article analyses critical integration scenarios across inventory management procurement sales and manufacturing processes detailing the technical enablers that facilitate seamless connectivity including embedded analytics API management and cloud platform integration Implementation considerations including master data harmonisation account determination configuration change management and third-party integration are examined through the lens of documented organisational experiences The article concludes by evaluating quantifiable business benefits and exploring emerging technologies such as artificial intelligence blockchain and IoT that are shaping the future evolution of finance-logistics integration in enterprise systems
Optimization of Frequency Reconfigurable Antenna Parameters Design Using Genetic and PSO Algorithms Based on Neural Networks
In this paper we propose a novel mixed-integer optimization formulation for the optimal design of a reconfigurable antenna inspired by methodology to design frequency reconfigurable patch antennas using multi-objective genetic algorithms MOGA genetic algorithm trained by recurrent neural networks and nondominated sorting genetic algorithm II NGSA-II improve global optimization capability by diversity detection operation to surrogate a model optimized Experimental validation of Pareto-optimal set miniaturized multiband antenna designs is also provided demonstrating a new optimization technique The oriented design here is practiced for improving reflection coefficient S11 and gain specifications at the frequency band that is achieved by sizing the design parameters using our proposed method in the author s way the performance parameters were predicted by an iterative process of particle swarm optimization based on feed-forward neural networks FFNN The proposed optimization technique is successfully attracted as a problem solver for designers to tackle the subject of antenna design which works in the frequency range from 200 MHz to 224 25MHz 50 impedance bandwidth at operated frequency 200 MHz sequentially is obtained
Copyright (c) 2024 Global Journal of Computer Science and Technology
Vehicle Routing Optimization with Ant Colony Optimization Algorithm Integrated with Map Analyzer API
Ant colony optimization ACO algorithm can be used to solve combinatorial optimization problems such as the traveling salesman problem In this work an endeavor has been taken in finding the proper algorithm which could be used for routing problems in different real-life situations Taking into due cognizance of the limitations of the existing routing system the outcome of this work will facilitate a more convenient way of finding destinations for the users in term of accuracy and time over the existing routing systems The cost of the program will also be lesser than contemporary systems To accomplish this a system has been built that can take a map image with source and destinations denoted and find an optimal path for them The work has been concluded with suggestions to future researchers who might look to build a system that can solve any type of routing problems using TSP
Constructing Classic Graphs in Graph Theory Using Python and Generative AI: A Case Study in Computational Visualization and Prompt Engineering
This study explores the construction of several classic graphs in graph theory through Python programming offering a hands-on computational approach to understanding their mathematical properties The selected graphs-including the Wagner Desargues Herschel M bius-Kantor Franklin truncated icosahedral and triangular grid graphs-are chosen for their historical significance and structural complexity Using Python s turtle graphics module each graph is visualized through trigonometric and geometric logic illustrating core concepts such as regularity symmetry Hamiltonicity and planarity In addition to manual code development the study integrates generative AI specifically ChatGPT to reproduce graph constructions via prompt engineering This dual approach showcases the educational potential of AI-assisted programming and reinforces algorithmic thinking The work aims to bridge the gap between theoretical graph concepts and their algorithmic applications It provides a replicable methodology that enhances student engagement supports active learning and promotes interdisciplinary exploration across mathematics computer science and education
Advancing Image Classification Performance: A Comprehensive Study of Modern Deep Learning Architectures on CIFAR-10
We present a comprehensive analysis of modern deep learning architectures for image classification on the CIFAR-10 dataset achieving state-of-the-art accuracy of 94 8 through an ensemble approach Our study evaluates five distinct architectural paradigms Enhanced ResNet 93 2 Modified DenseNet 92 8 Efficient-B0 variant 91 9 Vision Transformer adaptation 90 5 and a custom Hybrid CNN 92 4 We introduce a novel regularization strategy combining progressive dropout adaptive data augmentation and dynamic weight decay significantly improving model generalization Through extensive ablation studies and cross-architecture analysis we demonstrate that our ensemble method not only achieves superior accuracy but also exhibits enhanced robustness to input perturbations while maintaining computational efficiency Our findings provide practical insights for real-world applications and contribute to the ongoing discourse on architectural design choices in deep learning
Assessing Cybersecurity Response Readiness and Return on Security Investment Strategies Among SMEs in Ecuador
Cybersecurity remains a pressing concern for small and medium-sized enterprises SMEs particularly in developing nations where cultural structural and leadership barriers hinder proactive security adoption This study investigates the unique organizational and national culture dynamics influencing cybersecurity behaviors in Ecuadorian SMEs exploring how leadership traits risk perception and employee attitudes impact cybersecurity readiness through qualitative interviews with SME leaders across multiple sectors Using analytical frameworks such as Hofstede s cultural dimensions McClelland s motivation theory and dialectical organizational theory the research identifies five primary themes symbolic implementation of cybersecurity policies low formalization of risk response mechanisms lack of return-on-investment frameworks leadership styles that either support or hinder risk planning and widespread resistance to behavioral change Despite increasing exposure to cyber threats many SMEs remain reactive rather than strategic often lacking formal policies employee training or incident response plans Leadership personality traits play a significant role in shaping organizational prioritization of cybersecurity with achievement-oriented leaders driving more effective strategies
Leveraging Neural Networks for Longitudinal Analysis of Multiple Sclerosis and Other Neurodegenerative Diseases
Multiple Sclerosis MS is a progressive neurodegenerative disease affecting the Central Nervous System CNS leading to demyelination and neurological impairment Early diagnosis and continuous monitoring of disease progression are crucial for effective treatment Magnetic Resonance Imaging MRI remains the primary tool for detecting MS lesions however traditional segmentation methods rely heavily on visual analysis and struggle to detect early-stage lesions This study reviews the application of Convolutional Neural Networks CNNs for automated lesion segmentation in MS Through an integrative literature review of articles published between 2022 and 2024 from databases such as PubMed BVS Nature Arxiv and Google Scholar following PRISMA guidelines we assessed the effectiveness of AI-based approaches CNN models such as U-Net and nnU-Net demonstrated superior accuracy and sensitivity in segmenting lesions in FLAIR MRI images outperforming traditional methods Models like DeepLabV3 and ResNet also proved effective in differentiating between active and inactive lesions aiding in distinguishing acute from chronic lesions Automated segmentation reduced analysis time minimized false positives and enhanced reproducibility mitigating human variability in clinical evaluations While these advancements offer faster more accurate diagnoses and better monitoring of disease progression challenges remain Chief among them are the need for large-scale labeled datasets and standardization of MRI acquisition protocols Despite these obstacles the integration of AI-driven segmentation into clinical practice holds significant promise for improving MS diagnosis treatment planning and long-term patient management
Intelligent Ticket Assignment System: Leveraging Deep Machine Learning for Enhanced Customer Support
In the evolving customer support domain traditional ticketing systems struggle to meet increasing demands for speed and accuracy This study presents an intelligent ticket assignment system leveraging BERT Graph Neural Networks GNN and Prototypical Networks to enhance classification and routing efficiency The methodology includes comprehensive preprocessing of historical ticket data feature extraction using natural language processing NLP and model evaluation based on accuracy precision recall and F1-score Results indicate that BERT achieves the highest accuracy 89 4 precision 88 7 recall 90 2 and F1-score 89 4 outperforming GNN 87 6 and Prototypical Networks 86 8 by notable margins A comparative analysis with Random Forest 85 3 further demonstrates a 4 1 improvement in accuracy The analysis demonstrates both performance strengths and real-life practicality and scalability characteristics of the system when managing high traffic volumes Stability and predictive accuracy improved through the application of noise filtering alongside SMOTE oversampling and weighted loss functions for addressing data quality problems and class imbalance and model integration complexities The research demonstrates how machine learning changes the way customer service operations work while showing AI models can boost service quality and operational effectiveness
AI-Powered Generative Framework for Automated Clinical Audit Narratives: Regulated Prompt Engineering with LLMs and NLP
This paper explores an AI-powered framework designed to automate clinical audit narratives leveraging large language models LLMs and natural language processing NLP The system employs fine-tuned GPT models ICD-10-aware embeddings and regulated prompt engineering to ensure legal compliance This approach aims to enhance the accuracy efficiency and compliance of clinical documentation processes
Modeling LoRa Backscatter Communication Range
The enhancement of IoT applications for low-power and long-range communication requires the development of communication techniques that consume a small amount of power while transmitting at longer distances LoRa backscatter is a promising solution for such applications In this work we will develop a model that helps to estimate the communication range between a LoRa backscatter tag and a receiver The developed model has been tested by simulation using Python and the results are validated by comparing the achieved range using our model with state-of-art LoRa backscatter works We have also extended the model to account for the effect of SNR loss due to both direct interference from the transmitter and inter-tag interference from neighboring tags in concurrent LoRa backscatter systems
Revolutionizing Supply Chains: Cloud-Based Analytics, Blockchain, and Security Solutions for Innovation
The rapid evolution in technology advancements have resulted in massive transformation in the global supply chains industry These technologies bring in much needed innovative solutions that enhance efficiency transparency and security Cloud-based analytics blockchain technology and enterprise-level advanced security solutions are in the forefront of this evolving transformation Cloud-based analytics help supply chain stakeholders to make quick decisions based on data insight which are driven by real-time data processing with on-demand infrastructure Blockchain technology extends a decentralized and immutable ledger for better transparency traceability and security Using blockchain businesses can grow with decreasing fraud and enhancing trust between participants At the same time strong security measures safeguard valuable information and reduce the threat of cyberattacks maintaining the terms of data and confidentiality This paper explores the integration of these technologies to revolutionize how supply chains are designed and operated and displays a framework showcasing the proposed innovations to address contemporary challenges like inefficiencies frauds and cybersecurity issues and create sustainable yet adaptive supply chains of the future
Optimal Cooling Systems in Current Data Centers
echnological advancement has shown a vital impact in current world trends It has enabled communication information processing and collaboration among various aspects from personal use to departmental collaborations in workplace organisation Therefore it is important to understand the concepts of Information Technology IT infrastructure taking roles in evolving for the landscape of adaptability of information technology for the ever-changing demandson organisational needs IT infrastructure components such as the hardware software and the operating systems requires to have well designed IT infrastructure enhancing productivity security and accessibility Hence adapting to technological demands and changes with innovation 1