Harrison Blake

• Bachelor of science
• Harvard University

Gastroretentive Drug Delivery Systems (GRDDS) have emerged as a pivotal solution for enhancing the bioavailability and therapeutic efficacy of drugs with narrow absorption windows or localized action in the upper gastrointestinal tract. However, the complexity of GRDDS design-requiring the integration of pharmacokinetics, material science, patient...

The synergies between deep learning and self-supervised learning are examined in this study in order to enhance the real-time performance ability of the current cyber threat detection technologies. Advanced, adaptive threats that are becoming prevalent create a gap for traditional cybersecurity, therefore requiring the need for more advanced soluti...

Personalized medicine is revolutionizing healthcare by tailoring therapeutic strategies to individual patient needs. In this context, Gastroretentive Drug Delivery Systems (GRDDS) offer promising avenues for enhancing drug bioavailability, particularly for medications with narrow absorption windows in the upper gastrointestinal tract. This study ex...

Gastro-Retentive Drug Delivery Systems (GRDDS) represent a critical innovation in pharmaceutical science, enabling enhanced bioavailability and targeted drug release within the gastrointestinal tract. As research in this domain accelerates, managing and analyzing the vast, complex datasets generated presents a significant challenge. This paper prop...

Reservoir characterization plays a critical role in the efficient exploration, development, and management of hydrocarbon resources. With increasing complexity in reservoir geology and growing demand for enhanced recovery, traditional characterization methods are no longer sufficient to provide the resolution and accuracy needed for optimal decisio...

The increasing adoption of electric vehicles (EVs) poses challenges for power grid stability. This research introduces a GRU-LGBM hybrid model to forecast short-term load demand in EV charging networks. GRU captures time-based consumption patterns, while LGBM refines predictions by considering charging station occupancy rates, pricing policies, and...

The fast adoption of artificial intelligence within supply chain operations delivers multiple benefits including higher operational efficiency with automatic systems and better decision capabilities. The growth of these innovations creates additional security risks for companies. Artificial intelligence threats present in supply chains have surpass...

The investigation evaluates the vital relationship between AI security measures and privacy issues along with ethical limitations in reducing social engineering attack frequency. Organizations have started deploying AI systems to fight social engineering threats because these techniques utilize AI to manipulate people and get access to sensitive da...

Drug discovery has experienced a transformation through artificial intelligence (AI) systems which boost productivity while minimizing expenses and augmenting the efficiency of drug candidate selection. Traditional drug discovery approaches become less effective because they use many resources, cost too much money and fail frequently so AI provides...

Modern autonomous vehicle technology development has created critical cybersecurity difficulties at a fast pace. AI security systems provide promising protection measures which safeguard AVs against digital threats and their possible hacking attempts and data security breaches. Autonomous vehicles fall under increasing cyber-attacks because they in...

Ransomware attacks have experienced significant transformations through artificial intelligence which resulted in improved operational efficiency and advanced capabilities and adjustable behaviors. The cybercriminal world uses AI to create automated ransomware distributions as well as advance phishing tactics and boost encryption processes while ci...

Artificial Intelligence (AI) has emerged as a transformative force in financial risk management, providing sophisticated tools for risk assessment, fraud detection, and predictive analytics. Traditional risk management models often rely on historical data and predefined risk parameters, making them vulnerable to unforeseen financial crises. AI-driv...

Quick advancements in supply chain cyberattacks demonstrate the immediate requirement to implement strong infrastructure cybersecurity solutions. Modern supply networks adopt a business model that appeals to modern orchestrators due to existing vulnerabilities within their cross-vector systems. Artificial Intelligence (AI) defends critical infrastr...

Autonomous vehicles depend on interconnected hardware and artificial intelligence across multiple operational systems that creates new opportunities for seasoned cyber attacks. The article examines AI defense systems as essential technology to protect AVs from upcoming cyber threats. Adaptive algorithms combined with machine learning power systems...

Separate technology known as artificial intelligence (AI) functions as a disruptive conservation in precision medicine to deliver individualized healthcare. Artificial intelligence allows breakthroughs in diagnostics through its applications of large-scale data and advanced computational techniques as well as predictive modeling and treatment strat...

Artificial intelligence conducts rapid changes to social engineering operations as it creates new security challenges while also providing protection possibilities. Deepfake tools ranking as the biggest threat merge artificial intelligence-based tools which produce ultra-realistic artificial content that artificially duplicates human characteristic...

Modern cybersecurity needs Artificial Intelligence (AI) due to the quick development of cyber threats. The research explores a comprehensive comparison between conventional Artificial Intelligence methods and modern generative AI systems while evaluating their use for threat identification alongside defense capabilities. Traditional AI models based...

This study explores transfer learning in GRU-LGBM models to enhance short-term load forecasting across different regions. By pre-training on high-resource datasets and fine-tuning on local power consumption data, the model achieves improved generalization. Results indicate that transfer learning significantly reduces data requirements while maintai...

Large language models (LLMs) have advanced significantly in natural language processing and AI applications. While they are far superior in language generation, question answering, and summarization tasks, they are deficient in dealing with high-level problem-solving. While LLMs are capable of delivering coherent output, they are not capable of ada...

Modern cybersecurity needs Artificial Intelligence (AI) due to the quick development of cyber threats. The research explores a comprehensive comparison between conventional Artificial Intelligence methods and modern generative AI systems while evaluating their use for threat identification alongside defense capabilities. Traditional AI models based...

Ransomware-as-a-Service driven by artificial intelligence has become a major evolution in modern cyber threat development. RaaS enables cybercriminals to create a business-oriented ransomware deployment model open to both skilled and unskilled attackers who need tools and ransomware services. The combination of Artificial Intelligence with RaaS ena...

Brain tumors, among the most aggressive forms of cancer, present unique challenges in therapeutic management due to their location, heterogeneity, and resistance to conventional treatments. PARP inhibitors, a class of targeted therapies, have shown promise in improving outcomes in cancers with defective DNA repair mechanisms, such as those harborin...

Artificial Intelligence (AI) models, particularly Natural Language Processing (NLP) frameworks, have made remarkable strides in automating text summarization tasks. Despite their progress, these models encounter significant limitations when tasked with summarizing complex, nuanced, and domain-specific texts. This research explores the challenges fa...

Glioblastoma (GBM), the most aggressive and lethal primary brain tumor in adults, is characterized by its highly invasive nature, genetic heterogeneity, and resistance to standard therapies. Despite advancements in surgical techniques, radiotherapy, and chemotherapeutic approaches, the prognosis for GBM patients remains dismal, with median survival...

Gliomas, the most common primary brain tumors, represent a highly heterogeneous group of neoplasms with varying molecular profiles, clinical outcomes, and therapeutic responses. A hallmark of many gliomas, particularly high-grade variants such as glioblastomas, is the presence of defects in DNA repair pathways. These deficiencies render glioma cell...

The treatment of brain tumors has long been a challenge due to their complex biology, location, and resistance to conventional therapies. Radiotherapy, a cornerstone in cancer management, has proven effective in achieving local control in brain tumors. However, its efficacy is often hampered by tumor resistance and collateral damage to surrounding...

The rise of large language models (LLMs) such as GPT-4 and LLaMA has revolutionized natural language processing (NLP) applications. While these models exhibit impressive performance across various tasks, their explainability-the ability to understand and interpret how and why they generate specific outputs-remains a critical area of research. This...

Artificial Intelligence (AI) is revolutionizing the mobile application development landscape by enabling developers to create smarter, more intuitive, and efficient applications. The integration of AI into mobile development has introduced features such as personalized user experiences, predictive analytics, and enhanced security. This article expl...

Artificial intelligence (AI) has emerged as a transformative force in digital transformation, profoundly reshaping workforce skill development. This study explores the multifaceted impact of AI on workforce skill dynamics, emphasizing its role in fostering adaptability, promoting upskilling, and redefining traditional work paradigms. Employing a mi...

Human-machine collaboration in automated industrial environments has become a cornerstone of modern manufacturing and production systems. With the advent of Industry 4.0, the integration of intelligent machines, robotics, and human expertise is reshaping workflows, enhancing productivity, and improving operational efficiency. This article explores...

Industrial energy demand exhibits distinct patterns that require specialized forecasting techniques. This research applies a GRU-LGBM hybrid model to predict short-term industrial power consumption, incorporating production schedules, machine utilization rates, and environmental factors. GRU captures sequential dependencies, while LGBM improves fea...

The oil and gas industry faces significant risks, ranging from environmental hazards to operational inefficiencies and equipment failures. Accurate risk assessment is crucial to ensure safety, reduce operational downtime, and enhance decision-making. Recent advancements in machine learning (ML) offer innovative solutions to address these challenges...

The evaluation of machine learning models is a critical process in understanding their performance, reliability, and generalizability. Dataset diversity, encompassing factors such as data distribution, representation of minority groups, and feature variability, plays a vital role in determining the robustness and fairness of model evaluation metric...

The rapid advancement of artificial intelligence has enabled transformative applications in numerous domains, including natural language processing. One of the most prominent applications of AI in this domain is question answering (QA) systems. While these systems promise significant improvements in information retrieval, decision support, and educ...

This research examines the multilingual capabilities of GPT-4 and LLaMA, two leading language models designed to facilitate communication and problem-solving across multiple languages. Both models demonstrate remarkable achievements in processing and generating text in diverse linguistic contexts. However, their design, training datasets, and inher...

With rising concerns over data privacy, federated learning (FL) offers a decentralized approach to short-term load forecasting. This study develops a privacy-preserving GRU-LGBM model using FL, allowing multiple grid operators to collaboratively train models without sharing sensitive data. GRU handles sequential dependencies, while LGBM ensures rob...

Artificial Intelligence (AI) is transforming logistics and supply chain networks through its capacity to process vast datasets, identify patterns, and optimize decision-making. In an era characterized by globalization and technological advancements, traditional logistics systems struggle to meet rising demands for efficiency, agility, and sustainab...

Self-supervised learning (SSL) enhances AI models by leveraging unlabeled data. This study integrates SSL with GRU-LGBM for short-term load forecasting, allowing the model to learn representations from raw power consumption data before supervised training. Results show that SSL improves forecasting accuracy, especially in limited-label scenarios. T...

The role of movement in shaping predator-prey dynamics is a critical component of ecological studies, particularly the mechanisms driving these movements and their subsequent impact on ecological outcomes. Kinesis, a type of non-directional movement, plays a crucial role in predator-prey interactions, influencing both predators' foraging efficiency...

The dynamics of predator-prey interactions have long been a subject of interest in ecological modeling, providing crucial insights into the mechanisms that regulate population stability. In this paper, we explore the impact of saturation effects on predator-prey systems through the lens of the Beddington-DeAngelis functional response, a non-linear...

Childhood vaccination is a cornerstone of public health, yet immunization coverage in Delta State, Nigeria, remains suboptimal due to persistent barriers such as misinformation, cultural resistance, and limited maternal awareness. This study explores innovative strategies to improve maternal knowledge and engagement, aiming to increase childhood va...

Childhood immunization is widely acknowledged as one of the most effective public health interventions in preventing diseases that disproportionately affect children. Despite significant advancements in immunization coverage globally, regions such as Delta State, Nigeria, continue to experience challenges in achieving optimal immunization rates. Th...

Childhood vaccination is a cornerstone of public health initiatives globally, with vaccines preventing diseases that have the potential to devastate child populations. Despite concerted efforts in Nigeria to improve vaccination coverage, regions such as Delta State still struggle with suboptimal immunization rates. A critical factor influencing imm...

Childhood vaccination is a cornerstone of public health, yet immunization coverage in Delta State, Nigeria, remains suboptimal due to persistent barriers such as misinformation, cultural resistance, and limited maternal awareness. This study explores innovative strategies to improve maternal knowledge and engagement, aiming to increase childhood va...

In ecological modeling, predator-prey dynamics have been central to understanding the intricate relationships between different species in an ecosystem. The Beddington-DeAngelis functional response model, known for its non-linear and more realistic approach to describing predation rates, has gained significant attention due to its potential to exhi...

The rapid expansion of medical data has outpaced traditional diagnostic methods, necessitating advanced computational approaches for efficient and accurate disease detection. Semi-supervised learning (SSL) has emerged as a promising solution, blending labeled and unlabeled data to improve performance in resource-constrained healthcare settings. Thi...

Optimal foraging theory (OFT) provides a framework to understand the behavior and decision-making processes of predators in ecological systems. The theory posits that predators maximize their net energy intake by balancing the energy costs and benefits associated with prey capture and consumption. This research delves into the integration of OFT wi...

The delicate balance between predators and prey forms the backbone of many ecological systems, with their interactions driving biodiversity, ecosystem stability, and resource availability. However, human activities, including habitat destruction, overexploitation of resources, pollution, and climate change, have profoundly disrupted these dynamics....

Cloud computing has emerged as a transformative technology, enabling scalable and flexible access to computational resources. However, the efficient utilization of these resources remains a challenge, particularly in environments with high and unpredictable workloads. Load balancing algorithms play a pivotal role in addressing these challenges by d...

This study explores the role of Artificial Intelligence (AI) in combating online hate speech and extremism in North America, with a focus on the United States. The research investigates AI's capabilities in detecting and moderating hate content across digital platforms, addressing the growing volume and complexity of such harmful speech. It highlig...

Generative AI is transforming the cyber security landscape by introducing both unprecedented opportunities and severe challenges. On one hand, generative models such as GANs, transformers, and large language models (LLMs) are enhancing security systems by simulating attacks, identifying vulnerabilities, and improving anomaly detection. On the other...

Cyber threats have become even more complicated in recent years due to the ever-increasing sophistication of the attackers when avoiding the established security protocols. The growing complexity and uncertainty of cyberattacks are key drivers of the need for adaptive security systems capable of evolving through teaching and sensing recent threats...

Real-time Extract, Transform, Load (ETL) processes have emerged as crucial tools in enhancing fraud detection and risk management in financial systems. With the rapid evolution of financial technologies and the increasing complexity of fraudulent activities, traditional batch-processing methods have proven insufficient. Real-time ETL offers the cap...

The ever-increasing demand for energy efficiency in various sectors, including industrial, residential, and transportation, has prompted researchers to explore innovative solutions. Neural networks, a subset of artificial intelligence, have emerged as a powerful tool to optimize energy systems and minimize waste. This paper investigates the role of...

The rapid evolution of financial technology (FinTech) has necessitated the development of sophisticated data pipelines to process vast volumes of structured and unstructured data for predictive analytics and financial forecasting. Optimized data pipelines are critical in ensuring that financial institutions can derive actionable insights in real ti...

The integration of Artificial Intelligence (AI) into decision support systems (DSS) represents a transformative development in medical practice. AI-powered DSS leverage advanced algorithms and data analytics to enhance diagnostic accuracy, optimize treatment planning, and improve patient outcomes. This article provides an in-depth analysis of the a...

Artificial Intelligence (AI) is revolutionizing the operation and optimization of mechanical systems across industries. By leveraging AI, industries can enhance efficiency, reduce downtime, and improve system reliability. This paper explores the integration of AI-driven solutions in mechanical system operations, focusing on predictive maintenance,...

The FinTech industry is rapidly transforming, driven by the proliferation of data and the increasing need for sophisticated analytics. Cloud-based Extract, Transform, Load (ETL) solutions are emerging as critical tools in the development and optimization of data warehousing systems. This research explores how cloud-based ETL solutions are revolutio...

The rapid evolution of financial technology (FinTech) has driven the need for robust, scalable Extract, Transform, Load (ETL) strategies to handle massive datasets. With the growing adoption of big data analytics in FinTech, efficiently processing diverse and high-velocity financial data is critical for operational success and strategic decision-ma...

Personalization in email marketing has emerged as a key driver of engagement and conversion rates, particularly in the business-to-business (B2B) sector, where buyers demand relevance and value. This study explores the integration of generative artificial intelligence (AI) into personalized email campaigns for B2B marketing. Generative AI has demon...

The increasing demand for energy efficiency in industrial operations has led to the exploration of innovative technologies, particularly machine learning, for predicting energy consumption. This study investigates the application of machine learning algorithms to forecast energy usage in industrial machinery. By leveraging historical data and real-...

Predictive maintenance has emerged as a transformative approach for managing the health of mechanical systems, enabling businesses to optimize operations, reduce costs, and avoid unplanned downtimes. Machine learning (ML) has become a cornerstone in this field, offering sophisticated algorithms that can analyze large datasets to predict failures an...

As Artificial Intelligence (AI) systems become increasingly integrated into societal decision-making processes, concerns over fairness, equity, and bias have emerged as critical issues. AI technologies, often perceived as objective, can inadvertently perpetuate or even exacerbate biases present in data, leading to discriminatory outcomes. Algorithm...

Business-to-business (B2B) sales processes rely heavily on identifying and prioritizing highquality leads to optimize conversions and resource allocation. Traditional lead scoring methods often struggle with the complexity and dynamic nature of B2B sales funnels. This study explores the integration of artificial intelligence (AI) into lead scoring...

Predictive analytics has emerged as a transformative tool in healthcare, enabling practitioners to leverage historical and real-time data to make informed decisions that improve patient outcomes. This article explores the applications, methodologies, and challenges of predictive analytics in healthcare, focusing on how machine learning models, data...

The emergence of serverless computing has revolutionized the deployment and scalability of machine learning workflows. Serverless architectures allow developers to focus on model development and business logic while abstracting away infrastructure management. This paradigm shift presents new opportunities for optimizing resource utilization, reduci...

The integration of Artificial Intelligence (AI) chatbots into Customer Relationship Management (CRM) solutions has revolutionized the way businesses interact with customers. AI-powered chatbots have the potential to streamline customer support, enhance service efficiency, and improve customer satisfaction. This study explores the transformative rol...

The FinTech industry relies heavily on efficient data processing and analytics to support decision-making processes. As data grows in volume and complexity, Extract, Transform, Load (ETL) processes become integral to ensuring the accuracy, integrity, and usability of data stored in data warehouses. This article explores how advanced ETL processes c...

As artificial intelligence (AI) becomes increasingly integrated into decision-making processes across various industries, the need for transparency and explainability in AI systems has garnered significant attention. Transparency refers to the openness with which AI systems present their decision-making processes, while explainability focuses on pr...

The global demand for energy efficiency and sustainability has led to significant advancements in energy recovery systems (ERS) for mechanical applications. These systems are designed to capture, store, and reutilize energy that would otherwise be wasted, offering opportunities to enhance efficiency, reduce costs, and minimize environmental impact....

The financial sector operates in a highly regulated environment, where institutions must comply with an array of complex and evolving regulatory requirements. Traditional regulatory reporting methods, which rely on manual data collection, processing, and submission, are often inefficient, error-prone, and costly. Artificial Intelligence (AI) has em...

The rapid integration of artificial intelligence (AI) in financial services has significantly transformed the industry, enhancing efficiency, security, and customer experience. However, this progress has raised substantial concerns regarding data privacy, as AI systems rely on vast amounts of personal and sensitive financial information. This resea...

Financial institutions are prime targets for cyber threats due to the vast amounts of sensitive data they handle daily. Traditional security measures have proven insufficient in the face of increasingly sophisticated cyberattacks. Artificial intelligence (AI) has emerged as a powerful tool in cybersecurity, offering advanced threat detection and pr...

Artificial Intelligence (AI) has revolutionized the financial industry, offering powerful tools for risk assessment, fraud detection, trading, and customer service. However, its integration raises critical concerns related to bias, fairness, transparency, and ethical considerations. The presence of algorithmic bias in financial AI systems can lead...

The rise of Artificial Intelligence (AI) technologies has brought unprecedented opportunities in various sectors, from healthcare and finance to law enforcement and transportation. However, the increasing reliance on AI for decision-making raises critical ethical questions regarding accountability and transparency. AI systems, especially those base...

Persistence of threat of the zero-day vulnerabilities constitutes one of the greatest risks in cybersecurity as they target previously unknown flaws in any software. Established systems of security, such as signature-based, tend to be ineffective in identifying and resolving zero-day vulnerabilities on time. The promise of AI is that it can manipul...

The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize patient care by improving diagnostic accuracy, personalizing treatment, and optimizing resource allocation. However, the widespread use of AI in healthcare raises important ethical questions concerning patient privacy, data security, algorithmic bias,...

The application of artificial intelligence (AI) in criminal justice has rapidly increased, with predictive policing tools and sentencing algorithms being used to aid in decision-making processes. While these AI systems promise to improve the efficiency and fairness of the criminal justice system, they also raise significant concerns regarding bias,...

Data cleaning is a fundamental aspect of data preprocessing, ensuring accuracy, consistency, and reliability in datasets used for analytical and operational purposes. Traditional rule-based methods have been widely adopted, leveraging predefined conditions to detect and rectify inconsistencies. However, with the advancement of artificial intelligen...

Cloud storage solutions have become essential for handling big data, enabling enterprises to store, manage, and analyze massive volumes of information efficiently. Among the leading cloud storage services, Amazon S3, Google Cloud Storage, and Azure Blob Storage stand out as the most prominent platforms, each offering unique features, pricing models...

The exponential growth of data in large enterprises has necessitated robust data warehousing solutions that can efficiently manage, store, and retrieve vast amounts of structured and semi-structured data. Performance and scalability are two critical factors in determining the effectiveness of a data warehouse. This paper provides an in-depth review...

In recent years, deep learning models have demonstrated exceptional capabilities across various domains, including computer vision, natural language processing, and speech recognition. However, these models require vast amounts of labeled data for training, which is often expensive and time-consuming to obtain. Data augmentation has emerged as a po...

The increasing number of complicated threats to digital security has further made it impossible for conventional security systems to keep up with crafty cyber attacks. Using machine learning algorithms, the AI-based threat intelligence puts forth a viable solution to this issue, i.e. allowing fast threat detection, response, and anticipation. Howev...

With the increasing demand for artificial intelligence (AI) in portable and edge devices, optimizing energy efficiency has become a critical design challenge. Field-Programmable Gate Arrays (FPGAs) have emerged as a promising alternative to traditional CPUs and GPUs due to their reconfigurability and lower power consumption. This paper presents a c...

Reconfigurable computing has emerged as a promising paradigm for enhancing computational efficiency while addressing the stringent power constraints of embedded systems. Energy efficiency is a critical concern in modern embedded applications, particularly in battery-operated and remote systems where power resources are limited. Traditional hardware...

The rapid advancement of smart healthcare devices has revolutionized patient monitoring, diagnostics, and personalized medicine. However, one of the most pressing challenges in designing these devices is energy efficiency, particularly for battery-operated and wearable systems. Field-Programmable Gate Arrays (FPGAs) offer a promising solution due t...

Quantum computing represents a paradigm shift in computational power, promising revolutionary advancements across various domains. However, this disruptive technology also poses significant challenges, particularly in financial cybersecurity. Current encryption standards, foundational to securing financial transactions and sensitive data, may becom...

This paper explores the evolving landscape of cybersecurity threats in the financial sector. It identifies key emerging threats, analyzes their potential impacts, and discusses proactive measures to mitigate these risks. The study employs a comprehensive literature review and case studies to provide insights into the current state and future trends...

Wearable electronics have become integral to modern healthcare and personal monitoring systems, necessitating efficient power management to ensure prolonged operation and user convenience. Field-Programmable Gate Arrays (FPGAs) offer a reconfigurable platform that can be optimized for low-power consumption in wearable devices. This article delves i...

Edge computing has emerged as a transformative paradigm for real-time data processing, enabling low-latency responses and reducing dependency on centralized cloud infrastructure. Field-Programmable Gate Arrays (FPGAs) have become an essential component in edge computing due to their reconfigurability, high parallelism, and energy efficiency. Howeve...

Development of sophistication of cyber threats places traditional means of security, like signature-based systems, under pressure to respond fast enough to maintain par with instructive routes of attack. Reinforcement learning (RL) and adversarial learning are playing an essential role in cyber securities, offering unique benefits. Reinforcement le...

Customer engagement is a cornerstone of effective customer relationship management (CRM). The advent of artificial intelligence (AI) has revolutionized CRM systems, with AI-driven chatbots emerging as a key innovation for improving customer interactions. These chatbots leverage natural language processing (NLP), machine learning, and sentiment anal...

The rise of DevOps has transformed software development and IT operations, emphasizing automation, continuous integration, and continuous deployment. However, as systems grow more complex, the challenge of incident management has intensified, requiring proactive approaches to ensure reliability and performance. Machine learning has emerged as a pow...

The exponential growth of big data and its migration to cloud-based platforms necessitates robust security mechanisms to ensure data confidentiality, integrity, and availability. Cryptographic algorithms have emerged as fundamental tools for securing sensitive information within cloud environments. This paper explores the role of cryptographic algo...

The rapid growth of cloud and fog computing has revolutionized data processing and storage, enabling efficient service delivery across various sectors. However, these advancements introduce significant security challenges, especially concerning data breaches, unauthorized access, and insider threats. The traditional perimeter-based security model i...

The rapid evolution of e-commerce platforms has necessitated advancements in cloud infrastructure to handle the increasing demands for scalability, security, and performance. As e-commerce continues to grow globally, businesses require cloud-based solutions that can dynamically scale resources, provide high availability, and ensure robust security...

The adoption of cloud-native technologies is revolutionizing how e-commerce platforms are built and managed. As businesses move towards scalable, secure, and high-performance solutions, cloud-native technologies provide a powerful foundation for achieving these goals. This paper explores the role of cloud-native technologies, such as microservices,...

The increasing integration of Internet of Things (IoT) devices within cloud-based infrastructures has significantly amplified the volume and complexity of data exchanges, necessitating robust security frameworks to safeguard sensitive information. Traditional data exchange mechanisms often struggle with challenges such as data breaches, unauthorize...

The exponential growth of data in the digital age has brought significant opportunities and challenges, particularly in the realm of big data governance. The ability to collect, process, and analyze vast amounts of data has transformed industries, but it also raises concerns regarding data integrity and confidentiality. This research explores the c...