International Journal of Artificial Intelligence, Computer Science, Management and Technology
The International Journal of Artificial Intelligence, Computer Science, Management and Technology is a peer-reviewed, open-access journal dedicated to publishing high-quality, original research and advancements across its multidisciplinary focus areas. The journal serves as a global platform for researchers, academicians, industry professionals, and policymakers to exchange knowledge, ideas, and innovations in the domains of artificial intelligence, computer science, management, and technology. Journal particulars: Title: International Journal of Artificial Intelligence, Computer Science, Management and Technology ISSN: 3049-2483 Frequency: Quarterly (4 issues per year) Publisher: PG Publication House Publisher Address: 194,Shivsidh Residency, Ram Nagar, Maharajpura, Bhind Road, Gwalior, Madhya Pradesh (473005) Publication Place: Gwalior, Madhya Pradesh Publisher Email: ijacmtinternationaljournal@gmail.com Editor-in-Chief: Dr. Diwan Sher Singla Copyright: Remain Journal Starting Year: 2024 Main Subject: Computer Science Aim and Scope: Artificial Intelligence (AI), Computer Science, Management and Technology Language: English Publication Format: Online (Open Access) Phone Number: +919399044251 Website: https://ijacmt.com
Spatial Computing (AR/VR/XR) for Business Applications
Spatial computing, encompassing augmented reality (AR), virtual reality (VR), and extended reality (XR), is revolutionizing business operations by merging digital and physical environments. This research paper explores both doctrinal and non-doctrinal dimensions of spatial computing adoption in business applications. The doctrinal approach analyzes regulatory, legal, and standards frameworks—especially privacy, intellectual property, and platform interoperability—while the non-doctrinal method incorporates empirical data from global markets, corporate case studies, and industry reports. Findings indicate that the global spatial computing market is projected to reach over USD 418 billion by 2029, driven by demand in healthcare, manufacturing, education, and retail. AR dominates the technology segment due to its enterprise applicability, while North America and Asia-Pacific lead in adoption. However, high costs, data security risks, and lack of standards pose significant challenges. Using tables and graphs, this study presents real-time market insights and strategic implications for businesses. The research concludes with actionable recommendations for enterprise adoption, emphasizing a phased, ROI-based approach and alignment with evolving legal frameworks. Ultimately, spatial computing stands as a transformative force, unlocking immersive and data-rich environments for next-generation business innovation and collaboration.
Copyright (c) 2025 International Journal of Artificial Intelligence, Computer Science, Management and Technology https://creativecommons.org/licenses/by-nc/4.0
Natural Language Processing Using Transformer-Based Deep Learning Models
The advent of deep learning models based on transformers has revolutionized natural language processing by changing the way robots comprehend and produce human language. To express long-range dependencies in textual material effectively and enable parallel processing, transformers rely on self-attention mechanisms, unlike typical sequence models. Numerous natural language processing (NLP) tasks have benefited greatly from this architecture change. the function of transformer-based models in NLP, with an emphasis on designs like BERT, GPT, and associated variations. In this study, we look at how attention mechanisms improve scalability, language representation, contextual comprehension, and text production, machine translation, question answering, and categorization. It takes into consideration issues with computational cost, data needs, and the interpretability of the models. When compared to previous neural techniques, transformer-based models are far more accurate and flexible. The study wraps up by highlighting areas for future research that could help in making transformer-based NLP systems more efficient, easier to understand, and more ethical to use in the real world.
Artificial Intelligence-Based Predictive Models for Financial Risk Assessment
In banking, insurance, and investment management, where precise prediction of credit risk, market volatility, and default probability is vital for informed decision-making, financial risk assessment is a critical function that plays a significant role. Models of risk assessment that are considered traditional frequently rely on linear assumptions and narrow feature sets, which may not be able to adequately capture the complexity of contemporary financial systems. The development of predictive models that are able to analyze massive, complicated, and dynamic financial datasets has been made possible by recent advancements in artificial intelligence. Using machine learning and deep learning approaches to analyze credit risk, market risk, and operational risk, artificial intelligence-based prediction models for financial risk assessment are being developed. These models are centered on the evaluation of credit risk. In this context, the capacity of models that are routinely used, such as decision trees, support vector machines, ensemble approaches, and neural networks, to recognize nonlinear patterns and hidden risk factors is highlighted. In addition to this, it examines the difficulties associated with regulations, the interpretability of models, and the quality of the data. When compared to traditional methods, predictive models that are based on artificial intelligence offer enhanced accuracy and adaptability. This is especially true in financial situations that are highly volatile and data-intensive. An emphasis is placed in the conclusion of the study on the necessity of artificial intelligence systems that are transparent, resilient, and morally aligned in order to assist responsible financial risk management.
Convolutional Neural Networks for Image Recognition and Object Detection
Convolutional Neural Networks, also known as CNNs, have emerged as the most popular deep learning architecture for image recognition and object detection tasks. This is mostly owing to its capacity to automatically learn spatial hierarchies of features from visual data. Through the utilization of local connection, weight sharing, and pooling methods, convolutional neural networks (CNNs) are able to successfully capture patterns such as edges, textures, forms, and object structures. This makes CNNs particularly ideal for comprehensive picture analysis. the importance that CNN architectures play in image identification and object detection, with a particular emphasis on major models like as LeNet, AlexNet, VGG, and ResNet, as well as contemporary detection frameworks such as Faster R-CNN, YOLO, and SSD. The purpose of this article is to investigate how architectural changes can increase the speed of detection, the accuracy of classification, and the extraction of features. Additionally, it highlights issues that are associated with the complexity of computation, the demand for data, and the performance in real time. When compared to more conventional approaches to computer vision, the CNN-based models acquire a higher level of accuracy and robustness through their use. Following the conclusion of the study, the authors highlight the ongoing research areas that are targeted at enhancing the effectiveness, scalability, and interpretability of CNNs for the purpose of practical image recognition and object detection applications.
Sustainable IT Practices in Data Center Management
Data centers are at the core of today’s digital infrastructure but contribute significantly to global energy consumption and environmental degradation. This paper explores sustainable IT practices in data center management through a combined doctrinal and non-doctrinal research approach. Doctrinally, it analyses regulatory frameworks, corporate policies, and international sustainability standards such as the Climate Neutral Data Centre Pact. Non-doctrinally, it evaluates empirical data from 2023–2025, including global electricity usage, Power Usage Effectiveness (PUE), renewable energy adoption rates, and cooling innovations by tech giants like Google and AWS. Findings reveal a growing shift toward energy efficiency and carbon neutrality, with many firms investing heavily in clean energy and automation technologies. For example, global data centers consumed approximately 415 TWh in 2024, with projections nearing 945 TWh by 2030, driven by AI and cloud expansion. Strategies like server virtualization, immersion cooling, and DCIM systems have shown up to 74% energy savings potential. Despite progress, challenges remain in water management, infrastructure delays, and grid dependency. The study concludes by recommending integrated energy-water strategies, advanced cooling technologies, and policy-industry cooperation to ensure future-ready, sustainable data infrastructure.
Sustainable (Green) IT — Circular Data Center Practices
The rise of digital infrastructure—driven by cloud computing, AI, and IoT—has significantly increased the environmental footprint of data centers, which currently consume nearly 3% of global electricity and could reach 10% by 2030. This research critically explores the implementation of circular economy principles in data center operations, emphasizing energy reuse, material recovery, sustainable cooling, and strategic siting. Through a combination of doctrinal methodology (reviewing legal frameworks such as the EU Climate Neutral Data Centre Pact) and non-doctrinal research (industry case studies and data analysis), this paper examines the real-world effectiveness of circular data center practices. Key findings reveal that leading providers like AWS, Google, and atNorth have achieved Power Usage Effectiveness (PUE) as low as 1.1 and heat reuse rates up to 85%, while hardware circularity exceeds 99% in some cases. These practices not only reduce carbon emissions but also result in financial savings, enhanced compliance, and community co-benefits such as district heating and waste valorization. However, water usage in AI-intensive environments presents a growing concern. The paper concludes by recommending actionable strategies for policymakers, corporations, and urban planners to accelerate the global transition toward sustainable, circular IT ecosystems.
Role of Blockchain In Supply Chain Transparency
Supply chains are increasingly complex and globalized, necessitating advanced systems to ensure transparency, trust, and efficiency. Blockchain technology, with its decentralized, secure, and immutable ledger, offers a transformative solution to these challenges. This paper explores how blockchain can enhance supply chain transparency by providing traceability, preventing fraud, and fostering accountability. By examining key applications, case studies, and the challenges of implementation, the paper highlights blockchain's potential to revolutionize supply chain management. Despite its advantages, issues related to technological maturity, operational constraints, and regulatory compliance must be addressed for widespread adoption.
Deep Learning Approaches for High-Dimensional Data Classification
Traditional classification methods have been confronted with major hurdles as a result of the rapid increase in high-dimensional data created across a variety of areas, including bioinformatics, image processing, text analytics, and sensor networks. It is common for high-dimensional datasets to have problems such as the curse of dimensionality, feature redundancy, sparsity, and increasing computing complexity, all of which can have a negative impact on the performance of mathematical models. Because of its capacity to automatically learn hierarchical feature representations from big and complicated datasets, deep learning has emerged as a powerful strategy for high-dimensional data categorization. This is owing to the fact that it can automatically learn these representations. The categorization of high-dimensional data can be accomplished using a variety of deep learning techniques, such as deep neural networks, convolutional neural networks, recurrent neural networks, and autoencoder-based models. The research investigates the ways in which these designs tackle the problem of dimensionality by utilizing feature learning, representation compression, and nonlinear transformations. The performance of the model, its scalability, and its robustness are compared over a variety of data types, and comparative recommendations are made.
Performance Evaluation of Classical Machine Learning Models Versus Deep Neural Networks
There has been a surge in interest in comparing the performance of traditional machine learning models versus deep neural networks across a variety of analytical tasks as a result of the rapid development of data-driven applications. In the field of machine learning, traditional methods such as logistic regression, decision trees, support vector machines, and k-nearest neighbors have been highly regarded for a considerable amount of time due to their ease of use, straightforwardness, and effectiveness. Deep neural networks, on the other hand, have become increasingly popular as a result of their superior capacity to learn intricate nonlinear patterns from high-dimensional datasets that are both huge and complicated. A comparative analysis of the performance of traditional machine learning models versus deep neural networks with regard to the accuracy, scalability, computing cost, and interpretability of the results. In order to evaluate classification performance under a variety of data sizes and feature complexity levels, experimental analysis is carried out on datasets that are representative of the whole. According to the findings, classical models frequently achieve competitive performance on datasets that are either smaller or well-structured, but deep neural networks exhibit superior performance on data settings that are both large-scale and complicated.
Feature Engineering and Selection Strategies for Improving Machine Learning Accuracy
A significant contribution to the enhancement of the precision and dependability of machine learning models is made by the processes of feature engineering and feature selection. When dealing with complicated and high-dimensional datasets, the quality of the input features frequently has a higher impact on the performance of the model than the algorithm that is selected itself. Feature engineering is the process of transforming raw data into informative representations, whereas feature selection is the process of identifying the variables that are most significant, hence eliminating noise and repeated information. These techniques for feature engineering include normalization, encoding, feature building, and domain-driven transformations. Additionally, these techniques include feature selection strategies such as filter, wrapper, and embedding methods. This article will discuss how these approaches improve model generalization, decrease overfitting, and enhance computing efficiency across a variety of machine learning problems. An analysis of the impact of feature engineering and selection on both traditional and advanced learning models is presented, along with comparative observations for each.
Applications of Machine Learning in Healthcare Diagnosis and Prognosis
With its powerful data-driven capabilities, machine learning is revolutionizing healthcare by improving disease diagnosis and prognosis. There are new possibilities to improve clinical decision-making thanks to the proliferation of EHRs, medical imaging, genomic data, and real-time patient monitoring. Machine learning techniques are examined in this study for their potential use in healthcare diagnosis and prognosis, particularly in the areas of early disease detection, outcome prediction, and individualized therapy planning. frequently employed ML models, spanning supervised, unsupervised, and deep learning techniques, utilized in the fields of medical imaging, evaluation of clinical data, and risk classification. In particular, it looks at how these models help with timely interventions, decrease human error, and increase diagnostic accuracy. Problems include things like privacy worries, unintelligible models, poor data quality, and difficulties integrating into healthcare workflows. By combining clinical experience with machine learning-based solutions, diagnostic precision and prognosis dependability can be greatly improved. To guarantee safe and effective adoption in healthcare environments, machine learning solutions must be transparent, ethical, and clinically proven.
Bias Detection and Fairness Optimization in Machine Learning Algorithms
There are serious worries about prejudice and unfairness with the growing use of machine learning algorithms in decision-making systems. This is especially true in politically charged areas like healthcare, banking, hiring, and law enforcement. Machine learning models can be biased due to historical or imbalanced datasets, poor feature selection, or algorithmic design decisions. This bias can result in discriminating outcomes, which can worsen socioeconomic disparities. examines current methods for detecting bias and evaluating fairness, as well as the kinds and origins of bias in machine learning algorithms. Methods for reducing bias in models without sacrificing performance during pre-, in-, and post-processing. To evaluate the behaviour of algorithms, bias detection techniques are examined, including disparate effect analysis, statistical parity difference, and fairness metrics across protected attributes. In this article, we take a look at the pros and downsides of several fairness optimization methods, such as data re-sampling, adversarial debiasing, and constraint-based learning.
Hybrid Artificial Intelligence Models for Enhanced Predictive Analytics
One artificial intelligence technique has its limits when it comes to predictive analytics, especially with the increasing complexity of real-world data. Improved prediction accuracy, resilience, and flexibility can be achieved through the use of hybrid artificial intelligence models. These models integrate two or more computational methodologies. Hybrid models overcome the limitations of individual paradigms by combining the best features of many approaches, such as statistical methods, evolutionary algorithms, fuzzy logic, deep learning, and machine learning. the part that hybrid AI models play in improving predictive analytics in a wide range of contexts. It takes a look at some of the most popular hybrid designs, such as neuro-fuzzy systems, ensemble-based models, and hybrids of deep learning and classical machine learning. Representation of features, learning efficiency, and uncertainty decision-making are all enhanced by these models. Even in dynamic and complicated settings, the hybrid AI models always beat the standalone methods, especially when dealing with limited, noisy, or heterogeneous data. Building accurate and resilient predictive systems remains a difficulty when dealing with model complexity and interpretability. However, hybrid techniques provide a promising future in this regard. upcoming avenues for investigation that seek to enhance the transparency of predictive analytics and optimize the construction of hybrid models.
A Comparative Study of Supervised and Unsupervised Machine Learning Techniques for Large-Scale Data Analysis
Techniques for machine learning that are both effective and dependable have become more necessary as a result of the rapid expansion of large-scale data across a variety of areas, including business, healthcare, and social platforms. Despite the fact that supervised and unsupervised learning are two essential paradigms that are frequently utilized for data analysis, the comparative effectiveness of these two paradigms in large-scale settings is still an active topic of research. When applied to large-scale datasets, this study gives a comparative examination of supervised and unsupervised machine learning approaches with regard to their performance, scalability, interpretability, and computing efficiency. Specifically, the study focuses on whether or not the techniques are more effective. Using labeled data, supervised approaches such as regression, decision trees, support vector machines, and ensemble models are evaluated to determine their capacity to generalize and their level of predicted accuracy. Unsupervised techniques, on the other hand, are evaluated for pattern discovery, dimensionality reduction, and data structure identification without the use of prior labels. These techniques include k-means clustering, hierarchical clustering, principal component analysis, and density-based algorithms. The experimental research sheds light on the advantages and disadvantages of each method, indicating that supervised techniques typically produce higher predicted accuracy, whilst unsupervised methods give greater flexibility in terms of finding hidden patterns and lowering the complexity of the data. When deciding between supervised and unsupervised learning, it is important to take into consideration the availability of data, the goals of the challenge, and the limitations of the resources. It is possible that hybrid and semi-supervised algorithms have the potential to be useful solutions for large-scale data analysis, bridging the gap between prediction performance and exploratory insight.
Post-Quantum Cryptography in Corporate Data Protection
The rapid advancement of quantum computing poses a significant threat to classical cryptographic methods such as RSA and ECC, which underpin corporate data protection strategies worldwide. This research investigates the adoption and implications of Post-Quantum Cryptography (PQC) in corporate environments through doctrinal analysis of regulatory standards (NIST FIPS 203–205, NSA CNSA 2.0) and non-doctrinal insights from industry surveys and market reports. Despite growing awareness—69% of corporations recognize quantum threats—only 5% have implemented PQC solutions. The paper presents empirical data illustrating global market trends (projected CAGR ~37%) and evaluates sectoral preparedness, particularly within finance and government. Case studies from companies like Commvault, Apple, and NXP demonstrate early adoption, while challenges remain around integration complexity, cryptographic agility, and regulatory lag. The findings emphasize the urgency of a proactive shift towards hybrid encryption models, procurement reforms, and sector-specific PQC readiness frameworks. This research concludes that strategic, agile adoption of PQC is critical to safeguarding long-term corporate data confidentiality in the approaching quantum era.
AI-DRIVEN DECISION-MAKING IN BUSINESS OPERATIONS
In today's world, where the business environment changes every day, AI, together with BI, changes the decision-making process. This paper delves into the role and impact AI is having on traditional business analytics through BI that is driven by AI and what new concepts it brings to traditional business analytics, including accurate and real-time insight provision, enabler of predictive analysis and automation of data-intensive tasks. We explore some of the crucial technologies that have enabled this change, such as machine learning, natural language processing and more contemporary data mining methodologies. Also, the paper establishes the advantages of AI-integrated BI, which include efficiency in operations, business advantage, and the fact that they can reveal concealed patterns and trends that are hard to observe using traditional techniques. In this paper, we show how, with the help of AI-driven BI, companies can harness data better, make smarter decisions and attain their goals through case studies and real-life examples. Due to the progressive nature of the entrepreneurship world relying more on data, AI-driven business intelligence is well poised to help drive the emphatic future direction and success.
Blockchain & Smart Contracts for Transparent Collaboration
Blockchain and smart contracts are reshaping the foundations of trust and transparency in collaborative environments by offering decentralized, immutable, and verifiable mechanisms for data exchange and transaction automation. This paper investigates how these technologies facilitate transparent collaboration across diverse sectors such as supply chain, healthcare, finance, and Internet of Things (IoT) networks. Employing both doctrinal and non-doctrinal research methods, the study analyzes legal frameworks—like U.S. state-level recognition of smart contracts—and empirical adoption trends based on recent data from 2024–2025. The findings highlight blockchain’s potential to reduce reliance on centralized authorities, enhance data integrity, and automate contract execution with minimal human intervention. However, challenges remain in terms of legal enforceability, lack of regulatory uniformity, complexity of smart contract interdependence, and the risks posed by immutability in evolving business contexts. Real-world implementations like TradeLens, Chainlink, and Canton Network are critically examined to underscore both opportunities and systemic challenges. Tables and graphical visualizations further enrich the data analysis, revealing adoption patterns and market trajectories. The paper concludes that hybrid governance structures, privacy-preserving smart contract architectures, and legally harmonized standards are vital for unlocking blockchain's full potential in enabling transparent and trustworthy collaboration.
Internet of Things (IoT) for Smart Inventory Management
A smart inventory system is a computational time efficient system that helps businesses to manage and track their inventory levels, orders, and deliveries. It facilitates companies to have real-time visibility into their inventory and helps them make more informed and quick decisions about restock and how much to order. One key feature of a smart inventory system is its ability to automatically reorder items when they reach a certain threshold, eliminating the need for manual intervention. This helps to ensure that businesses always have the right amount of inventory on hand, reducing the risk of running out of stock or having excess inventory that takes up valuable storage space. This paper includes deployment of this system in real world which benefits to handle smart inventory system with improved accuracy 30% and efficiency in inventory tracking around 10%, reduced lead times for ordering and restocking, and the ability to track inventory across multiple locations. This paper briefly elaborates the implementation of smart inventory system that greatly improves a business's inventory management process, leading to increased profitability by more than 50%, average foot fall increased to 25% and reducing the waiting time of customer by nearly 75% making customer more satisfied.
Internet of Things (IoT) in Supply Chain and Management Systems
The integration of the Internet of Things (IoT) in supply chain and management systems has significantly transformed traditional logistics and operational models into intelligent, data-driven ecosystems. This research explores the multifaceted applications of IoT in enhancing real-time tracking, predictive maintenance, inventory optimization, and sustainability across global supply networks. Employing both doctrinal (literature and statutory review) and non-doctrinal (case studies, market data, stakeholder insights) methodologies, the paper presents a comprehensive analysis of IoT-enabled efficiencies—such as Schneider Electric’s 18.7% delivery improvement and Amazon’s 40% fulfillment cost reduction—while also examining critical challenges, including cybersecurity risks, interoperability issues, and the high cost of adoption. Real-world deployments from 2023–2025 demonstrate how firms achieve measurable gains through strategic implementation of IoT, AI, blockchain, and edge computing. The findings underscore the urgent need for standardized protocols, collaborative frameworks, and sustainable governance models to realize the full potential of IoT in supply chain ecosystems. With the IoT-enabled logistics market projected to surpass USD 55 billion by 2031, the study advocates for adaptive strategies that align technological innovation with ethical practices, transparency, and resilience in future global supply networks.
AI Governance Platforms & Ethical Tech Oversight
As artificial intelligence (AI) systems increasingly influence critical aspects of society, the demand for robust governance and ethical oversight has intensified. This research paper explores the evolving landscape of AI governance platforms and ethical tech oversight through a dual-method approach combining doctrinal and non-doctrinal analysis. The doctrinal method reviews recent international regulations, including the European Union AI Act, the Council of Europe’s Framework Convention on AI, ISO/IEC standards, and U.S. state-level initiatives. Simultaneously, the non-doctrinal method examines recent corporate surveys, market trends, and academic studies to assess the practical uptake of governance tools and ethical protocols. Real-time data reveals a significant increase in corporate governance initiatives—77% of surveyed organizations report active governance programs—while the AI governance software market is projected to grow at a 49.2% CAGR by 2034. However, institutional capability gaps and regional regulatory fragmentation pose major challenges. The study concludes that a harmonized, capacity-driven, and lifecycle-sensitive governance model is essential for ensuring ethically aligned AI deployment. The findings serve as a foundation for policymakers, corporate leaders, and academic researchers aiming to design accountable and future-ready AI ecosystems.
ADVANCED SASS AND LESS USAGE IN DYNAMIC UI FRAMEWORKS
In the dynamic and fast-paced realm of front-end web development, the need for scalable, maintainable, and responsive user interface (UI) solutions is increasingly critical. CSS preprocessors like SASS (Syntactically Awesome Stylesheets) and LESS (Leaner Style Sheets) have become instrumental in addressing these needs by introducing variables, functions, mixins, nesting, and modular architecture into traditional CSS workflows. This paper explores the advanced usage of SASS and LESS in conjunction with dynamic UI frameworks such as React, Angular, and Vue. It delves into their role in creating component-based design systems, efficient theming structures, and modularized stylesheet architectures. Special focus is given to features like control structures, reusable style patterns, and performance enhancements via preprocessor-based optimizations. Moreover, the study evaluates real-world implementation patterns, best practices, and pitfalls to avoid, backed by insights from blog articles, documentation, and industry case studies. The integration of SASS and LESS into modern frontend workflows not only simplifies code complexity but also enhances maintainability, responsiveness, and developer productivity. By highlighting the synergy between preprocessors and modern UI frameworks, this research underlines their indispensable value in professional web development environments.
THE INTERSECTION OF FAMILY-BASED IMMIGRATION AND DOMESTIC VIOLENCE: EXAMINING VAWA SELF-PETITIONS AND THEIR ROLE IN PROTECTING VULNERABLE IMMIGRANTS
This research paper explores the intersection of family-based immigration and domestic violence in the United States, focusing on the role of the Violence Against Women Act (VAWA) self-petition provisions. Family-based immigration often places noncitizen spouses, children, and parents in vulnerable positions where abusers exploit their dependency for legal status to exert control and perpetuate harm. The VAWA self-petition mechanism, introduced in 1994 and expanded through subsequent reauthorizations, enables survivors of domestic violence to independently seek lawful permanent residency without the cooperation or knowledge of their abuser. This paper critically examines the eligibility criteria, evidentiary challenges, and administrative pathways involved in the self-petition process. It highlights the legal and emotional barriers that prevent many survivors from accessing protections, including fear of deportation, lack of awareness, and difficulty in securing documentation. The paper further evaluates the effectiveness of support systems, such as legal aid and nonprofit organizations, and recommends policy interventions to enhance survivor safety and legal empowerment. Ultimately, VAWA self-petitions serve as a vital legal remedy that empowers immigrant survivors to reclaim autonomy, yet continued systemic reforms are necessary to ensure accessibility and justice for all victims of abuse within the immigration system.
THE CONVERGENCE OF DEVOPS, DATA SCIENCE, AND AI IN SOFTWARE DEVELOPMENT
This report focuses on the interaction and the impact that can be achieved when DevOps, Data Science and AI are adopted in the software development process. However, by reviewing relevant literature and evaluating the existing practices in software development fields, several aspects that may improve development processes are revealed. These complex combined elements contribute to innovation, enhancement of software quality, and effective decision-making as pointed out by our research. However, some challenges should be overcome; these may include the shift in cultures and technical issues. Finally, it presents suggestions for practitioners and offers questions for future research to enhance the development of this interprofessional perspective.
Data Migration Strategies for Cloud-Native Applications: A Framework for Large-Scale Enterprises
Cloud-native computing emerges as a result of the growth of cloud computing delivery paradigms. As the most important online application development paradigm, cloud native computing has already garnered more and more interest from both business and academics. A defined research path on this issue is still lacking, despite the enthusiasm in the cloud-native industrial community. Adoption of state-of-the-art methods, technologies, frameworks, tools, and infrastructure is necessary for the creation of contemporary software applications. Although each category offers a wide range of alternatives, the common features include reduced cost, lightweight and scalable applications, and quicker delivery. Furthermore, testing, deployment, and operational automation have grown in importance. Considering how accessible and reasonably priced data is, cloud computing has a lot to offer. Because users often store sensitive data in cloud storage providers, which may not be trustworthy, ensuring cloud data security is an important consideration in the world of cloud computing. There is now a tendency towards "interclouds," also known as "multi-clouds" or "cloud-of-clouds." The organisation will see significant growth and profitability if it can operate cloud-native apps. The impact of cloud native security on business outcomes is significant. The architecture of cloud-native security is also covered in this article. This article highlights the new methods for creating and implementing Security-as-a-Service (SecaaS) applications leveraging cloud-native design paradigms. The immediate threat to computer systems and applications is not adequately addressed by the most recent SecaaS solutions. This issue is resolved by cloud-native design patterns, which combine micro service architectures with cloud-focused interface design to provide features like substantial optimisation and durability.
IOT AND AI FOR SMART WAREHOUSING AND REAL-TIME INVENTORY MANAGEMENT
The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) has revolutionized warehouse management by enabling real-time inventory tracking, predictive analytics, and enhanced automation. This research paper explores the transformative impact of IoT and AI in modern warehousing, highlighting technological advancements, operational benefits, and challenges. By examining case studies of leading organizations, this paper illustrates the role of smart warehousing in optimizing logistics, reducing costs, and improving overall supply chain efficiency. The findings underscore the necessity for businesses to adopt these technologies for sustainable growth in an increasingly digital economy.
IoT, Artificial Intelligence, Smart Warehousing, Inventory Tracking, Predictive Analytics, Supply Chain Optimization
REINFORCEMENT LEARNING FOR WAREHOUSE MANAGEMENT AND LABOR OPTIMIZATION
The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has revolutionized warehouse management and labor optimization. Among the various AI methodologies, Reinforcement Learning (RL) has emerged as a powerful tool to address complex logistical challenges by enabling intelligent systems to learn and adapt dynamically. This paper explores the role of RL in warehouse management, emphasizing dynamic order picking, robotic sortation, labor management, and overall optimization. The research incorporates case studies from leading industry players, analyzing real-world applications of RL in improving operational efficiency, reducing costs, and enhancing labor productivity. Furthermore, this paper examines the challenges and future implications of RL adoption in warehouse settings, providing insights into how this technology can shape the future of logistics and supply chain management.
Event-Driven Architectures: Leveraging AWS for Scalable Cloud Solutions in Large-Scale Enterprises
Software development has changed significantly as a result of the rise of public cloud computing platforms. Applications' software design must match the features of cloud services in order to benefit from many of the advantages offered by cloud platforms. Therefore, it is often not viable to migrate HPC applications to the cloud by recreating traditional HPC clusters using cloud infrastructure. We examine the particular use of seismic imaging as an alternative to the general lift and shift method, and we show how to execute large-scale instances of this issue on the cloud using an event-driven and server-less approach. FaaS (Function as a Service) platforms are among the most well-known services in recent years. Their main selling point is how simple it is to deploy little bits of code in certain programming languages to carry out particular activities in an event-driven manner. With a fine-grained pay-per-use model, these operations are carried out on the Cloud provider's servers, saving customers from having to bother about upkeep or elasticity management. While edge computing analyses data close to its source, decentralizing data processing and reducing data transfer to centralized servers, the Internet of Things entails linked devices collecting and sharing data on their own. Scalable and secure edge computing and Internet of Things infrastructures are made possible by AWS. AWS Lambda allows server-less computation, enabling effective deployment and scalability of IoT applications; AWS Green Grass expands AWS capabilities to edge devices; and AWS IoT Core controls IoT device connection and data intake. Large amounts of IoT data often cause problems for centralized cloud infrastructures. By decentralizing data processing, edge computing improves real-time capabilities, lowers latency, and uses less bandwidth. Through AWS IoT Core, which supports many protocols for smooth integration with IoT devices, AWS guarantees secure device communication. For applications with strict latency constraints or restricted connection, AWS Green Grass enables local data processing and machine learning at the edge. Scalable, event-driven designs without server administration are made possible by AWS Lambda's server less computing technology, which is essential for varying IoT workloads. To sum up, AWS develops IoT capabilities at the edge, with real-world applications in a variety of sectors. AWS continues to play a key role as IoT develops, coming up with new ideas to satisfy changing needs for IoT deployment.
A Machine Learning Approach to Intrusion Detection in Multi-Cloud Environments: Enhancing Cybersecurity
Cybersecurity concerns have grown in tandem with the use of multi-cloud environments, which enterprises are using to improve scalability and flexibility. As a result, organizations must have strong intrusion detection systems to protect themselves. This paper introduces a method for intrusion detection that uses machine learning and is specifically built for multi-cloud architectures. Its purpose is to detect and react to suspicious behaviors that occur across different cloud platforms that are connected. In order to identify cyber dangers and illegal access in real time, the suggested model examines massive volumes of data from cloud traffic using techniques including ensemble approaches, supervised learning, and anomaly detection. When tested in a multi-cloud environment, our machine learning strategy demonstrated to be more accurate, faster to respond, and more adaptable than conventional intrusion detection methods. enhances cybersecurity in multi-cloud settings by providing a solution that can grow with changing threat landscapes and proactively protects against new cyber threats.
Copyright (c) 2024 International Journal of Artificial Intelligence, Computer Science, Management and Technology https://creativecommons.org/licenses/by-nc/4.0
Optimizing Deep Reinforcement Learning for Real-World Robotics: Challenges and Solutions
Deep Reinforcement Learning (DRL) has shown immense potential in enabling robots to learn complex tasks through trial and error, without explicit programming. However, deploying DRL in real-world robotic systems presents significant challenges, including sample inefficiency, safety constraints, and the transferability of simulated results to physical environments. This paper explores the key obstacles in optimizing DRL for real-world robotics and proposes solutions to address these issues. We examine advancements in sim-to-real transfer, efficient exploration strategies, and safety-aware learning to enhance the applicability of DRL in robotics. Additionally, we discuss the integration of model-based and model-free DRL approaches to improve learning speed and performance. Through experimental evaluation on robotic manipulation and navigation tasks, we demonstrate that optimizing DRL for real-world scenarios requires a combination of robust simulation environments, hybrid learning architectures, and careful management of exploration-exploitation trade-offs. Our findings provide valuable insights for deploying DRL in autonomous systems, industrial robotics, and human-robot collaboration, ultimately bridging the gap between simulation and real-world performance.
Optimizing Data Augmentation Techniques for Improved Generalization in Machine Learning Models
In situations when labeled data is limited or unbalanced, data augmentation has become an essential method for improving machine learning models' ability to generalize. In order to make models more resilient and less prone to overfitting, augmentation approaches generate different versions of the training data. better generalization across various machine learning tasks through optimizing data augmentation methodologies. From more conventional approaches like geometric changes and noise injection to cutting-edge methods like adversarial training and neural style transfer, we explore it all. We test several augmentation strategies extensively on image classification, NLP, and time-series prediction tasks to see how they affect model performance. We show that optimum data augmentation, when adjusted for dataset specifics, greatly improves model robustness and accuracy when dealing with out-of-sample data. In addition, we offer a new approach to augmentation that integrates augmentation rules with automated search algorithms, allowing for strategies to be dynamically adjusted throughout training. In practical settings with uneven or scarce data, our findings pave the way for stronger machine learning models.