This article explores the use and impact of voting using blockchain technology. As a result of mounting worries about the honesty and security of conventional voting methods, especially in the digital era, the study tackles the requirement for an open, impenetrable, and robust method of conducting elections. A decentralized application on a blockchain platform is designed and implemented using the technique, which makes use of smart contracts for voter registration, ballot generation, and vote counting. The viability of blockchain technology in (...) guaranteeing voter anonymity, data integrity, and cryptographic security are important discoveries. Online voting can be done securely and digitally using blockchain technology. It has great potential to reduce costs and increase voter turnout. Using Blockchain we can secure any application and work securely for that application. Therefore, cryptocurrency, healthcare, real estate, etc. Many applications use blockchain technology. *Blockchain technology encrypts votes, preventing any votes from being tampered with. It allows voters to vote for a candidate only once. The system reduces labor costs and counting errors by providing quick access to election results. This article explains solutions to solve the above problems. Our solution is to use an online voting syst. (shrink)

The health industry is experiencing change, the newest forerunner of which is being propelled by spatial computing and generative AI. Spatial computing simply refers to the ability to interface with physical space through computation and digital devices; on the other hand, generative AI means using advanced machine learning to generate new output. This paper examines the roles and the combined possibilities of these two technologies with the view of transforming health care and diagnostics in the field of patient care. Precision (...) medicine unveils new opportunities of integrating precise spatial computing components and generative artificial intelligence to provide a personalized treatment for patients and data visualization for surgeons. The paper examines major use cases, concerns, and future of this convergence specifically in the fields of medicine. (shrink)

Today e-commerce has had tremendous growth in the past years primarily due to changes in technology and customer’s buying behavior. One of the big shifts in the process has been the use of ML in advertising which has the capability to transform the marketing domain together with consumer interactions. This paper discusses the viability of using machine learning for designing realistic models of advertising to increase effectiveness of target and personalized advertising, as well as conversion rates in e-commerce. Several techniques (...) are explored in the study, such as supervised and unsupervised learning, recommender systems, and content optimization with natural language processing. By using case studies and experimental models, we discuss how and to what extent ML is beneficial in e-commerce advertising transformation. The results presented in this paper indicate that using machine learning in advertising has a potential of dramatically improving the customer experience while simultaneously increasing brand recognition and sales. (shrink)

Generative AI has proven itself as an efficient innovation in many fields including writing and even analyzing data. For spatial computing, it provides a potential solution for solving such issues related to data manipulation and analysis within the spatial computing domain. This paper aims to discuss the probabilities of applying generative AI to graph-based spatial computing; to describe new approaches in detail; to shed light on their use cases; and to demonstrate the value that they add. This technique thus incorporates (...) graph theory, generative models to model spatial relations, generate new spatial forms and improve on spatial decision-making processes. The paper surveys such methods, describes typical applications, and outlines further development of the subject. (shrink)

Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally (...) accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies. (shrink)

: Serverless computing has revolutionized the way modern cloud-native applications are developed and deployed, offering a paradigm in which developers can focus solely on writing code without the burden of managing infrastructure. Among the various Function-as-a-Service (FaaS) offerings, AWS Lambda stands out as a widely adopted platform due to its automatic scaling, event-driven execution, and pay-per-use pricing model—making it ideal for applications ranging from microservices to complex event-driven workflows. However, serverless architectures present challenges in cold start latency, performance tuning, and (...) cost optimization, particularly when using heavyweight runtimes like Java or under sporadic invocation patterns. This study systematically evaluates AWS Lambda’s performance under varying load conditions by analyzing runtime performance (Java vs. Python), memory allocation, payload size, and traffic types including steady, burst, and spiky patterns. Key metrics such as cold start time, average execution duration, and cost per invocation are examined using real-world traffic simulations and observability tools like AWS CloudWatch and X-Ray. The findings demonstrate that tuning memory allocation, applying provisioned concurrency, and selecting lightweight runtimes can significantly improve latency and cost-efficiency. The paper concludes with best practices for serverless workload optimization, offering actionable insights for software architects and DevOps teams aiming to balance scalability, responsiveness, and operational cost in cloud-native deployments. (shrink)

How do we know what we know? • How do we perceive ourselves and the world around? • Is what we perceive the only reality that exists? (or) • Is there a different reality other than what we perceive? (Not referring to the source of C, but to the CoC, in the very limit of our physical structure).

The generative AI system is being adopted across the several fields to provide novel solutions for text generation, image synthesis, and decision-making. But when they are used in multi-agent and multi-cloud systems, they are expensive in terms of computation and finance. Regarding the aforementioned factors, this paper aims to examine methods of reducing such costs while achieving system efficiency. Such measures as dynamic workload distribution, resource scaling, as well as cost-conscious model selection is described. Through the examples of case studies (...) and simulations, we show that incorporating these strategies can drastically decrease expenses and ensure immediate and accurate scalability across clouds of different ecosystems. (shrink)

CVDs continue to be the number one cause of death, therefore early detection and treatment is crucial. Currently, the application of Artificial Intelligence (AI) in the cardiac image analysis has become popular due to increased accuracy, productivity, and modelling. In this paper, the use of the AI system to study the echocardiogram, CT, MRI, and other images of the heart and blood vessels to view the risk factors for cardiovascular diseases is discussed. We focus on the current methods of artificial (...) intelligence, how it is implemented in clinical practice, problems, and prospects for AI development. (shrink)

Many emerging applications require a higher level of flexibility, modularity, and efficiency but are dependent on advancements in communication infrastructure and distributed computing. Time-sensitive networking (TSN) standards aim at providing vendor agnostic, reliable, and deterministic communications over the Ethernet, but lack in flexibility and modularity provisions. In this context wireless communication systems are preferred given the obvious benefits in terms of increased flexibility, reduced deployment & maintenance costs, and inherent mobility support. However, the stochastic nature of the wireless medium poses (...) several challenges in achieving these benefits. In this paper we comprehensively analyze the recent standardization efforts and developments in IEEE 802.11 and 5G to enable low-latency, deterministic communications and present the current status of their integration with wired TSN. Then, we present a set of use cases that may be enabled by wireless TSN including industrial automation,automotive,or audiovisual applications . (shrink)

By integration with cloud computing, cybersecurity and generative AI, the insurance industry is being transformed from high efficiency, low cost, and better customer service. However, these advanced technologies can also be used by insurers to automate and streamline processes like claims handling, underwriting, and policy generation, which are majorly time consuming and error prone. In predictive analytics, fraud detection, and personalized customer experience, generative AI makes it possible for insurers to mitigate risks and, at the same time, provide more personalized (...) services to customers. With cloud platforms, data management is scalable, flexible and secure, offering real time access to data that drives value by accelerating sound decisions in the insurance value chain. Sensitive data is protected against cybersecurity threats, staying compliant with regulations and marginally reducing risk. Guidewire’s unified technology stack allows insurers to leverage the integrated nature of these technologies through end-to-end integration to improve operational resilience and customer satisfaction. It is these synergies that are the future of insurance which is why transparency, ethical practices in AI as well as proactive cybersecurity are vital. It will become an arms race for insurers to keep pace with these changes and move away from being rigid and slow to adapt; they will need to stay agile and quick, especially with the landscape changing so rapidly. (shrink)

The contribution of this paper is a comprehensive cybersecurity framework to secure cloud hosted Guidewire implementations by addressing critical security challenges such as threat detection, incident response, compliance, and system performance. Based on advanced technologies like machine learning, behavioral analytics and auto patching, the framework detects and mitigates known and unknown threats, incidentally zero-day exploit. The system does this through micro segmenting, behavioral anomaly detection, and automated patch orchestration in a way that does not render the system unperforming. Key performance (...) metrics of threat detection time (less than 3 seconds), incident response (patching within 5 minutes) and system availability (99.95% uptime) are all tested as the framework outperforms in all of these. Additionally, the framework ascertains industry standards such as GDPR and HIPAA via automated audit trails and ongoing monitoring. Compared to the currently deployed cloud security, the proposed approach, with a special focus on more secure the Guidewire application deployed in the cloud, offers a multi layered security approach to the modules specific to the Guidewire. Though powerful, the framework also suffers from complexity in its deployment, overhead in terms of performance and a reliance on cloud provider-specific features. The scalability will need to improve, and the product will need to support multiple clouds and integrate advanced technology such as quantum computing and AI-powered threat intelligence. This work sets a solid basis for the protection of cloud-based Guidewire systems against future cyber threats while maintaining operational continuity and system compliance. (shrink)

The digitization process has brought new opportunities in insurance industry operations and innovations but has also revealed major weaknesses. Since more and more actual insurers use Guidewire to handle claims, policies, and customer data, insurers become targets for cyber threats that target valuable information. The framework of Guidewire, along with cloud computing integrated API and third-party tools, is laden with numerous exposure points. These security gaps are utilized to execute phishing, spread malware and gain unauthorized access to customers and operational (...) data. The profound changes in this environment require an evolution of concepts and methods dedicated to cybersecurity from the traditional, post-incident response to comprehensive, targeted strategies for cybersecurity that are appropriate for the insurance sector. To explore these challenges, this paper looks at current cybersecurity frameworks and methods, specifically in the context of Guidewire. The gaps within the existing system are thus determined through the interaction of empirical analysis and real-life scenarios to arm the research with the ability to formulate a solution to fill the gaps. Including threat-detection suites safe system design, the study aims at the antecedents of building protection-based methodologies that provide real-time protection and cope with new forms of cyber threats. Citing this strategic angle, it becomes possible to corroborate the reality that technology development in the insurance industry should not be pursued at the peril of strong security systems in place to support the new breed of insurance systems. (shrink)

In today’s fast moving insurance environment, efficient claim management is a vital business practice to improve operational effectiveness and provide an exceptional customer experience. Based on the first notice of loss (FNOL), Guidewire Claim Center, the leading claims management platform, gives insurers the thorough, scalable, and customizable option to accelerate the claims process from FNOL to final resolution. In this paper, we explore how insurers adapt Guidewire Claim Center to enable effective utilization of Guidewire Claim Center to optimize and transform (...) their claim management process, making better decisions, and delivering better customer experience. Advanced automation features, their role in reducing manual footprint, and the integration of the platform with external systems for a smooth data flow and analytics driven insights are key things covered. Finally we see real world case studies detailing where Guidewire's predictive analytics are in use for fraud detection, expedited claims processing and building proactive relationships between the claimant and the Carrier. We also look at best practices for implementation and ways to work around typical problems, including change management, system customization, and others. Framed around the dual aims of operational efficiency and customer satisfaction, this blueprint offers practical advice for insurers navigating a more and more digitalized industry. Guidewire Claim Center is agile, flexible foundations that can help insurers transform their claims operations, while also creating lasting trust and loyalty with policyholders. (shrink)

Origin of life on earth transpired once and from then on, it emerges as an endless eternal process. Matter and energy are constants of the cosmos and the hypothesis is that the origin of life is a moment when these constants intertwined or interacted. Energy from the cosmos interacted with inorganic matter to support matter with retention of this riveted energy, as energy to be circulated within the primitive channelized structures to conserve energy by the materialization of the proton homeostasis (...) mechanisms developed from the obtainable inorganic matter. The driver for these processes as we now confirm, exists in the quantum world and through quantum phenomenal processes could have combined these constants to create the magic of life. Primitive earth was a chemical reactive system that triggered a macromolecular evolution by means of open thermodynamic systems, driven by cyclic gradients of temperature, electromagnetic radiation and chemical potentials which sustained life and proto-consciousness in the first life forms driven by the quantum processes. The origin of life is always an intriguing topic but the purpose for finding the cause should never be inclined towards obliterating it; for if that is the case, the further we seek, the farther it will go. (shrink)

Generative Artificial Intelligence (AI) is rapidly emerging as one of the most innovative and transformative technologies in the field of machine learning. Unlike traditional AI, which focuses on classification and prediction tasks, generative AI is designed to create new, original content, such as images, music, text, and even software code. This paper delves into the foundational concepts of generative AI, highlighting its core models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models. We explore the applications of generative (...) AI in various domains such as art, healthcare, and entertainment, and examine the ethical considerations surrounding its use. Additionally, the paper discusses the future potential of generative AI in fostering creativity, automating workflows, and addressing complex real-world problems. Th. (shrink)

The Home Depot data breach, which occurred in 2014, marked a significant turning point in the cybersecurity landscape, drawing attention to the increasing prevalence of ransomware and cyberattacks targeting large-scale retail organizations. This breach exposed the personal and financial information of over 56 million customers and highlighted the vulnerabilities in the systems used by major corporations. The attack was attributed to a sophisticated ransomware operation that gained access through a third-party vendor’s compromised credentials, ultimately enabling cybercriminals to infiltrate Home Depot’s (...) network. This paper explores the Home Depot breach, focusing on its relation to the rise of ransomware as a common cyber threat. It delves into the tactics, techniques, and procedures (TTPs) employed by the attackers, the vulnerabilities exploited, and the broader implications for the retail industry and cybersecurity at large. Additionally, the study examines how ransomware has evolved from a relatively niche threat to a significant weapon in the cybercriminal arsenal, affecting organizations across all sectors. By analyzing the breach and its aftermath, this paper offers insights into best practices for cybersecurity resilience and incident response strategies that can help mitigate future threats of similar magnitude. (shrink)

The increase in claim sophistication in both the insurance and legal domains is a result of an increase in stokes and heterogeneity of data needed to assess the claim validity. Originally, this task was performed by some sort of subjectivity assessments and graphical rule sets, which is very slow and may be inherently erroneous due to its purely manual nature. Hence, with progressivity in multimodal learning, specifically in AI, there is now a unique chance of solving these challenges through the (...) use of text data, which may include policies, reports and images, which may include accident images, evidence images, videos such as surveillance, cam videos among others. However, existing AI-based solutions usually address only one of the modalities, which makes it difficult to evaluate an integrated situation. This has led to the need for systems that will integrate information from all these modalities and come up with an accurate, efficient, and transparent processing system. Indeed, this paper seeks to discuss the use of Multimodal generative AI to address this need as one of the most recent approaches that rely on high-performing models that can process and integrate text, image, and video data. The proposed system combines these modalities to ensure that the system captures relevant data from each data type and combines all in a way that provides more comprehensive and enriched decision support. An initial system was designed and empirically tested against current claim adjudication techniques and was found to yield substantial enhancements in all utilization rates, throughput and main rationale for the claim decisions. The findings shown in the study stress the capability of multimodal generative AI for revolutionizing the present approaches of claims analysis and developing more efficient, accurate, and capable responses to various real-life conditions. This integration of technologies is an unprecedented advance towards advancing functional processes in the insurance and legal industries. (shrink)

P&C insurers have an important role in addressing financial risk management needs but now struggle to respond to the new forms of risk. Historical analysis and actuarial calculations, which form the backbone of classical approaches to risk measurement and management, are not well suited to such new kinds of risks as climate change, cyber risks, and business cycle risks. These conventional approaches are also a static method for selling, which has limited potential in changing quickly with new market and consumer (...) changes. However, the development of AI, big data, or advanced predictive analytics has provided a new paradigm to such challenges to cope with and analyze big data dynamically to derive Applying a combination of best Machine Learning (ML), natural language processing (NLP) and other AI techniques, it will become possible to predict risks exposures, as well as optimize underwriting and claims processing. In this paper, the author investigates the opportunities that AI carries within the sphere of reformation of risk evaluation approaches in P&C insurance. In particular, it discusses the ever-emerging field with AI models integrated into operations, underwriting, fraud detection, claims modelling, and loss computation. The applied research uses a comprehensive bibliographical review to present past and present practices and assess modern AI implementations. This paper uses research approaches and case studies to prove that AI can solve some problems and bring cost-saving solutions. Moreover, it emphasizes the ability of predictive analytics to enhance customer satisfaction at the policy level as well as optimize operations. However, the paper also explores the dirty side of implementation, including the stipulations surrounding compliance and data privacy and thus provides an objective analysis of the future of AI use in the P&C Insurance Industry. (shrink)

Many trading desks across the financial industry still rely heavily on Excel-based tools for pricing, risk assessment, and reporting workflows. While these spreadsheets offer flexibility and familiarity, they present challenges in scalability, collaboration, version control, and integration with modern trading infrastructure. This paper presents a cloud-native migration framework that transforms trader-centric Excel tools into robust, scalable applications using Java and Python. The framework incorporates modular microservices, RESTful APIs, AWS services, and real-time data processing. It also addresses migration challenges such as (...) formula translation, data validation, and end-user adoption. The resulting system improves performance, auditability, and maintainability while retaining the usability traders expect. (shrink)

This abstract summarizes, in essence, what generative AI means to the insurance industry. The kind of promise generated AI offers to insurance is huge: in risk assessment, customer experience, and operational efficiency. Natural disaster impact, financial market volatility, and cyber threat are augmented with techniques of real time scenario generation and modeling as well as predictive simulation based on synthetic data. One of the challenges that stand in the way of deploying these AI methods, however, is data privacy, model reliability (...) and interpretability. The insurance industry must address these issues if it is to comply with regulations like GDPR and CCPA or mitigate reputational risks, as anybody can make complaints about any company based on any perceived data breach. Indeed, generative AI models, often convoluted and biased, can propagate unfair outcomes, especially in pricing and policy access. Navigating these challenges requires a whole new frayed society of AI specialists in collaboration with the industry’s insurers to offer bespoke solutions that address the firm’s needs. Based on this work, future developments will center on designing and implementing ethical and regulatory frameworks around AI-driven decisions that are transparent, fair and sustainable. Generative AI has historically been the most feared phenomenon in the history of western civilization, but with predictive analytics and risk simulation capabilities taking shape, generative AI is set to disrupt the insurance industry and add real-time decision support, automating claims processing and personalized interaction. To successfully integrate generative AI, technical, ethical and regulatory challenges will need to be addressed, and a trust-driven ecosystem will emerge to serve evolving customer needs. (shrink)

Traditional steganography systems suffer from outdated compression and encryption methods, leading to large key sizes, high computational complexity, and increased vulnerability to steganalysis attacks. These limitations make conventional steganography inefficient and less secure. Additionally, advancements in AI-driven image analysis have made image steganography increasingly ineffective, as modern algorithms can easily detect hidden data within images. In contrast, audio steganography remains an underexplored field with significant potential, though existing solutions are often underdeveloped and lack robust security measures. To address these challenges, (...) we propose an Enhanced Audio Steganography System that integrates modern lossless compression algorithms (e.g., Zstandard, Brotli) with state-of-the-art encryption techniques (e.g., AES-256, ChaCha20). Our system employs Least Significant Bit (LSB) steganography, enhanced with dynamic algorithm selection and randomization, to maximize security and efficiency. By intelligently combining compression, encryption, and adaptive embedding techniques, our approach significantly improves imperceptibility, robustness, and adaptability, making it ideal for secure communication and covert data transfer. By leveraging advanced cryptographic and compression techniques, our system achieves high security with minimal computational overhead, effectively overcoming the limitations of traditional methods while ensuring efficient and secure audio- based data concealment. (shrink)

The proposed project is a smart parking reservation system that offers clients an easy way to reserve a parking space online. It overcomes the problem of Finding a parking area in industrial or commercial areas that is useful consumes time. As a result, this challenge provides an internet-based reservation machine where customers can view numerous parking regions and select the space to view whether an area is to be had or not. If the reserved space is available, he can book (...) it for a specific time slot. The booked area might be marked yellow and could no longer be available for all of us for the specified time. This gadget affords the additional characteristic of cancelling the bookings. Each time, the user can cancel their books area. Users can make payment online via Credit card, UPI, etc. Users are notified approximately after making the payment, the booking via email along with a particular parking number. Online Vehicle Leaving Framework, as portrayed above, can prompt mistake free, secure, solid and quick administration framework. (shrink)

Generative artificial intelligence (Gen AI) and machine learning (ML) technologies are changing the way the insurance industry looks, particularly by integrating Gen AI & ML technologies. As a leading platform for property and casualty insurance, Guidewire provides a perfect platform for deploying intelligent claims processing workflows that can dramatically improve efficiency, accuracy and customer satisfaction. The seamless integration of Gen AI and ML capabilities into Guidewire to autonomously process claims is explored in this paper. Insurers can use advanced models to (...) drive self-driving workflows that are capable of automating tasks like claims triage, fraud detection, damage assessment and settlement optimization. But most importantly, these technologies reduce operational costs, and reduce the human intervention involved such that claims can be handled faster and with more accuracy. In this work, we discuss what technical and strategic considerations need to be taken into account when implementing such workflows, such as data integration, model training, and ethical AI practices. This paper also demonstrates several real-world use cases, the challenges you in each, and the potential to scale these solutions across lines of business. With an eye on the future, autonomous claims processing enables insurers to find their next competitive edge by providing innovative solutions that alter the course of expectations of customers while qualifying to regulatory and compliance standards. Self-driving workflows powered by Gen AI and ML in claims are the future of insurance and their ability to transform the entire process of claims management. (shrink)

The success of AI models largely depends on the availability of large and high-quality datasets. However, in many real-world scenarios—such as medical diagnostics, remote sensing, or low-resource languages— gathering such data is difficult or even impossible due to privacy, cost, or scarcity. Synthetic data offers a compelling alternative. This paper proposes a comprehensive synthetic data pipeline tailored for data-scarce domains. It includes domain understanding, synthetic data generation using simulation, generative models (e.g., GANs, diffusion models), data augmentation, validation, and integration with (...) downstream AI models. Experimental evaluation in healthcare imaging and industrial defect detection demonstrates improved model accuracy and generalizability using synthetic data pipelines. (shrink)

Zero-day vulnerabilities are critical security risks that exploit previously unknown flaws in software systems, making them difficult to detect and defend against. As such vulnerabilities are discovered by attackers before the software vendor is aware of them, they provide a significant advantage to malicious entities. The exploitation of zero-day vulnerabilities is a major concern for both individuals and organizations, as it can lead to unauthorized data access, loss of information, and compromised systems. This paper aims to explore the nature of (...) zero-day vulnerabilities, their discovery and exploitation, and the current strategies for mitigating these risks. By reviewing existing literature and case studies, the research identifies the core challenges in addressing zero-day vulnerabilities, discusses existing countermeasures, and evaluates the potential future direction of cybersecurity techniques aimed at reducing exposure to these threats. The study highlights the need for continuous software monitoring, rapid patch deployment, and enhanced awareness in the security community to mitigate zero-day risks effectively. (shrink)

Recent studies show that racism still exists in the American medical profession, the fact of which legitimizes the historically long-legacy of mistrust towards medical profession and health authorities among African Americans. Thus, it was suspected that the participation of black patients in end-of-life care has always been significantly low stemmed primarily from their mistrust of the medical profession. On the other hand, much research finds that there are other reasons than the mistrust which makes African Americans feel reluctant to the (...) end-of-life care, such as cultural-religious difference and genuine misunderstanding of the services. If so, two crucial questions are raised. One is how pervasive or significant the mistrust is, compared to the other factors, when they opt out of the end-of-life care. The other is if there is a remedy or solution to the seemingly broken relationship. While no studies available answer these questions, we have conducted an experiment to explore them. The research was performed at two Philadelphia hospitals of Mercy Health System, and the result shows that Black patients’ mistrust is not too great to overcome and that education can remove the epistemic obstacles as well as overcome the mistrust. (shrink)

The project offers a text summarization tool that can handle content from PDFs, DOCX, and plain text. It incorporates extractive and abstractive summarization methods for creating abridged summaries. The extractive method applies sentence tokenization in the selection of important sentences, whereas the abstractive method utilizes a pre-trained Hugging Face model. The tool also has a Streamlit-based user interface for handling multiple file uploads and direct input of text. Strong error handling and text cleansing improve dependability and summarization accuracy, so the (...) tool is useful for applications needing effective text analysis like in healthcare, research, and legal arenas. (shrink)

In this paper, we present a case of a boy – Noah Wall, who till today surprises the world of neuroscience with his will to grow his brain and survive. The case presented in this study sets a stepping stone in understanding the advent of the will to make a choice, from a neuro-quantum mechanics interpretation. We propose that besides our internal states of choices (neurogenesis, neuroplasticity, cell differentiation, etc.) we also relate with external states of choices (love, compassion, empathy, (...) emotions, etc.) that contributes to its emergence. Quantum uncertainty seems to support the existence of a fundamental property based on which the universe functions; which means that even the nothing of free space has a small chance of containing something. Outcomes are not determined by prior or random events but by consciousness that gives rise to these outcomes. This provides us a lead into understanding the existence of the will and the origin of choice when we look deeper into the realms of the implausible interpretations of quantum mechanics. Free will is the ability for the mind to choose between possible outcomes. Willful power is therefore not only a psychological intervention but also a biological and quantum intervention, where we have the capacity to make choices about what direction we will take, making a change to the systematic functioning of our body. (shrink)

This remains the case because cyberattacks are becoming more frequent and sophisticated, and as a result, the IDS must also be innovative and evolving to protect sensitive networks. Traditional intrusion detection techniques are replaced by more advanced and dynamic Methodologies based on Machine Learning (ML) algorithms. These dynamic systems capture significant data flows, search for potentially pathological patterns, and forecast threats in real time. By simulating and analyzing real-time cases, this paper discusses the architecture for ML-based IDS, how this function (...) is implemented, and its effectiveness. The primary issues of data quality, model scaling, and interpretability are presented with solutions to address them and support the reliable functioning of the system. (shrink)

Accurate and timely validation of trade inputs is critical in high-frequency commodity and financial trading environments. Manual or delayed validation can result in erroneous trades, compliance risks, and operational inefficiencies. This paper presents a real-time input validation system for traders, implemented using low-latency Java APIs and AWS Kinesis. The system ensures that inputs are checked against business rules and market constraints in milliseconds before proceeding to execution workflows. Our architecture leverages AWS Kinesis for high-throughput data streaming, Java-based microservices for synchronous (...) rule enforcement, and DynamoDB for persistent rule management and transaction tracking. The system demonstrates strong scalability, low latency, and adaptability across diverse trading desks. (shrink)

The Internet of Things (IoT) is a rapidly growing technology that connects and integrates billions of smart devices, generating vast volumes of data and impacting various aspects of daily life and industrial systems. However, the inherent characteristics of IoT devices, including limited battery life, universal connectivity, resource-constrained design, and mobility, make them highly vulnerable to cybersecurity attacks, which are increasing at an alarming rate. As a result, IoT security and privacy have gained significant research attention, with a particular focus on (...) developing anomaly detection systems. In recent years, machine learning (ML) has made remarkable progress, evolving from a lab novelty to a powerful tool in critical applications. ML has been proposed as a promising solution for addressing IoT security and privacy challenges. In this article, we conducted a study of the existing security and privacy challenges in the IoT environment. Subsequently, we present the latest ML-based models and solutions to address these challenges, summarizing them in a table that highlights the key parameters of each proposed model. Additionally, we thoroughly studied available datasets related to IoT technology. Through this article, readers will gain a detailed understanding of IoT architecture, security attacks, and countermeasures using ML techniques, utilizing available datasets. We also discuss future research directions for ML-based IoT security and privacy. Our aim is to provide valuable insights into the current state of research in this field and contribute to the advancement of IoT security and privacy. (shrink)

PostgreSQL has established itself as a powerful and feature-rich open-source relational database management system, increasingly adopted in modern cloud-native applications. As organizations transition to serverless computing for scalability and operational efficiency, integrating traditional stateful systems like PostgreSQL within stateless, event-driven workflows poses unique performance and architectural challenges. This research investigates the performance characteristics of PostgreSQL in such environments by leveraging key AWS services, including AWS Lambda and Amazon S3, to orchestrate serverless data pipelines. The study simulates a range of real-world (...) scenarios, including high-throughput bulk data inserts, concurrent data access, and complex analytical queries, to assess PostgreSQL’s behavior under load in an event-driven setup. Performance metrics such as query latency, throughput, system scalability, and cost-effectiveness are examined in depth. Furthermore, we present a case study modeled on production-scale workloads to highlight common bottlenecks, the impact of cold starts, database connection limits, and strategies to mitigate these issues through architectural and configuration optimizations. The findings contribute actionable insights for developers and architects designing scalable and efficient serverless systems with relational databases. (shrink)

Through a combination of Guidewire platforms, cloud computing and cybersecurity frameworks, the insurance industry in miniature is being transformed into a digitally transformed, ever resilient ecosystem. This ecosystem enables insurers to modernize core operations of policy management, claims processing and billing while continuing to provide secure, scalable and efficient service delivery. Insurers using cloud infrastructure have elastic resources capable of scaling to meet dynamic workloads and can provide high availability and fast disaster recovery. They have liberated data analytics to run (...) on the cloud with advanced capabilities to churn out actionable insights to optimize operations and create personalized offerings for their customers. Sensitive customer data and important systems are securely protected from cyber security threats by a robust cybersecurity layer, which includes data encryption, controls access, and detects threats proactively. Integration and cloud adoption challenges like costly implementation, cybersecurity risk and legacy system migration will be handled by deft and effective strategies such as phased adoption, workforce upskilling and adoption of a zero-trust security model. Within operational efficiency, stronger resilience and innovative business models enabled, i.e. behavior-based insurance pricing through IoT and real time analytics, are the opportunities. However, moisture and regulatory complexity can be barriers to change, and investments in modern technology, workforce development, and partnerships with insurtech and cybersecurity experts can enable insurers to survive and thrive in a more competitive, digital-first landscape. This convergence engenders customer trust, unlocks its operational agility and ensures compliance with changing requirements, thereby enabling insurers to sustain their growth, build resilience and rival digital transformation. (shrink)

The digital transformation of the insurance industry is unprecedented, urged by the changing expectations of customers, the necessity to embrace regulatory requirements and the necessity to enhance operational efficiency. This paper examines the convergence of three leading-edge technologies, Guidewire, Amazon Web Services (AWS) and Snowflake, to enable scalable, resilient and data-driven solutions for insurers. AWS and Snowflake provide unmatched cloud infrastructure in terms of security and scalability. They also provide industry-specific applications and frameworks for modernizing core systems for Guidewire, such (...) as Snowflake data warehousing and analytics. By working together, these platforms allow insurers to leverage the combined power of integrated ecosystems, real-time insights, and automation to meet current challenges and adapt to future disruptions. A key focus of this study is providing a roadmap for insurance would-be insurgents looking to capitalize on agility, customer-centricity, or both in one of the most rapidly changing industries in terms of distribution. (shrink)

Property and Casualty (P&C) insurers are adopting multi-cloud environments as a strategic imperative because of their increasing data volumes and complexities in regulatory compliance, customer expectations and technological advancements. This paper discusses how Snowflake’s cloud-agnostic, unified platform enables insurers to create resilient, efficient, and compliant multi cloud data strategies. Using Snowflake’s elastic scalability, real-time analytics, secure data sharing and seamless cloud interoperability, insurers can optimize claims processing, augment fraud detection, and support customer engagement. The study offers core design principles for (...) multi cloud data architectures and sheds light on problems in data fragmentation, latency, and security risks. It points to how Snowflake enables data resilience, including data replication, encryption, GDPR, and CCPA compliance. Details are provided. Practical implementation strategies of phased data migration ETL pipelines and integration with existing enterprise system to ensure a smooth deployment. Snowflake is seen as a clear win through a cost-benefit analysis with metrics like RTO and RPO that define how well Snowflake performs disaster recovery and business continuity. Additionally, the paper also looks at the emergence of tech like AI, blockchain and edge computing which could additionally bolster multi-cloud strategies even further. Finally, Snowflake enables the journey for P&C insurers through cloud ecosystem pitfalls, enabling operational resilience, regulatory compliance, along competitive advantage in a data-driven industry. (shrink)

This paper examines the challenges and lessons learned for Property and Casualty (P&C) insurers as they look to balance technological innovation against regulatory compliance, specifically regarding privacy laws. These technologies have been adopted rapidly, from Artificial Intelligence (AI), blockchain, and the Internet of Things (IoT), to transform how claims are processed, resulting in efficiencies, improved fraud detection and increased customer-centeredness. While these advancements have brought much to the table, they hold great fear regarding the data privacy, security, and compliance involved. (...) Insurers need to incorporate privacy-centric frameworks during the development and deployment of new technology to help them navigate these challenges. Our key strategies are adopting the Privacy by Design approach, ensuring compliance-focused innovation via regulatory sandboxes, and constantly monitoring the evolution of privacy regulations. Mitigating privacy risks is possible only when you have effective governance, such as having robust data governance policies and third-party risk management. Additionally, leadership is committed to helping create an environment that is privacy-centric, and providing employee training and transparency in terms of communication with the customer is equally important to sustaining trust. The paper uses a case study of Shift Technology’s AI-based fraud detection system to show how innovation can meet privacy and compliance goals. Finally, the final section concludes with a discussion of emerging privacy risks (cybersecurity threats and algorithmic bias), along with identifying the crucial role of a strong organizational culture in addressing these threats. At the end of the day, this paper emphasizes the importance of adapting privacy-preserving technologies, working with regulators, and monitoring privacy practices to ensure continuous compliance and create innovation in claim processing. (shrink)

Organizations are migrating their policy management systems from the ground to the cloud, changing how they manage, secure and scale their operations. This paper analyses how Amazon Web Services (AWS) and Snowflake support robust and efficient cloud-based policy management solutions. This marriage of sort of AWS, which has an almost panoply of cloud services, and Snowflake, which is very well known for its data warehousing and analytics capabilities, makes sense because it is an enterprise in which you will want to (...) modernize your policy management. Ways to explore Amazon’s infrastructural offerings, such as IAM (Identity and Access Management) and Security Hub, are also discussed as key components in the path to governance and compliance in a cloud environment. Snowflake’s ability to merge and process large sets of data worldwide to drive dynamic policy changes, predictive analysis, and real time insight punts is unique. Finally, this paper discusses the critical challenges this paper addresses, ranging from security, high interoperability, and cost optimization, and how these platforms address these hurdles and features, such as Zero Trust, automated scaling, and native integration with enterprise systems. Using case studies and the best practices, this analysis offers actionable insights for organizations that want to use AWS and Snowflake to manage cloud policy. These findings indicate that a combined approach using AWS and Snowflake looks to improve our operational efficiency and ensure we comply with the existing framework to be ready for future advancements in the cloud native policy ecosystems. (shrink)

As the integration of Artificial Intelligence (AI) and cloud computing transforms the insurance industry, it is undergoing a major breakthrough. With these technologies, insurers can modernize operations, improve the customer experience and make better decisions using real time data and predictive analytics. This paper aims to explore why AI and cloud play such critical roles in shifting insurance practice from legacy systems modernization, to data governance and regulatory compliance to workforce readiness. Today, world-class AI powered tools and cloud platforms such (...) as AWS and Snowflake provide hyper personalization, one-touch automated claims, fraud detection, etc. IoT data can be used to perform dynamic risk assessment for insurers and even to offer products that start with low code and no code solutions to make technology available across an organization. Edge computing, as well as sustainability across insurance products, shows that there is more innovation potential. Yet, these technologies cannot be fully integrated. However, the industry must figure out how to navigate evolving regulations, employ explainability in AI-driven decisions, and address vendor dependency issues. Governance frameworks need to be strengthened;the workforce needs to be upskilled, collaborate with InsurTech startups, and so on. The paper ends with a forward-looking view, offering potential expansion into underserved markets, more advanced predictive analytics, and better customer contact through omnichannel support via AI enabled risk coaching. Embracing these advances, insurers can chart a new course and lead the digital transformation era by providing more value to stakeholders in an evolving environment. (shrink)

This article examines the rapidly evolving landscape of cybersecurity, focusing on emerging threats and innovative defense mechanisms. We analyze four key threat vectors: Advanced Persistent Threats (APTs), ransomware, Internet of Things (IoT) vulnerabilities, and social engineering attacks. These threats pose significant risks to organizations, including data breaches, financial losses, and operational disruptions. In response, we explore cutting-edge defense mechanisms such as Artificial Intelligence and Machine Learning for threat detection, Zero Trust Architecture for access control, blockchain for data integrity, behavioral analytics (...) for insider threat detection, and quantum cryptography for enhanced encryption. The article also outlines best practices for cybersecurity, including regular software updates, strong password policies, employee training, and security audits. By synthesizing current research and industry trends, this article provides a comprehensive overview of the cybersecurity landscape, emphasizing the need for continuous adaptation and vigilance in the face of evolving cyber threats. (shrink)

As more people utilise online banking services, the large losses that banks and other financial institutions sustained because of new bank account (NBA) fraud are concerning. Machine learning (ML) models have faced significant challenges because to the intrinsic skewness and rarity of NBA fraud cases. This occurs when the number of non-fraud instances exceeds the number of fraud instances, causing the ML models to miss and mistakenly regard fraud as non-fraud instances. Customers' confidence and trust may be damaged by such (...) mistakes. Existing research addresses the skewness of fraud datasets by considering fraud patterns rather than possible losses of NBA fraud risk characteristics. This study suggests NBA fraud detection in the framework of value-at-risk, a risk metric that views fraud cases as the worst-case situation. Value-at-risk models risk features as a skewed tail distribution and estimates possible losses of such attributes using historical simulation. ML was used to classify the risk-return characteristics derived from value-at-risk on the bank account fraud (BAF) dataset. The value-at-risk assigns weight to the skewed NBA fraud cases by managing the fraud skewness with an adjusted threshold probability range. The effectiveness of the fraud detection algorithm was assessed using a unique detection rate (DT) metric that takes risk fraud characteristics into account. A K-nearest neighbour with a true positive (TP) rate of 0.95 and a DT rate of 0.9406 is used to create an enhanced fraud detection model. Value-at-risk offers a clever way to create data-driven standards for fraud risk management within a reasonable loss tolerance in the banking industry. (shrink)

As artificial intelligence (AI) systems become increasingly integrated into decision-making processes across critical domains—such as healthcare, finance, criminal justice, and employment—questions surrounding their trustworthiness, ethical implications, and potential for bias have taken center stage. While AI promises enhanced efficiency, accuracy, and scalability, it also poses significant risks if ethical considerations are overlooked or if systems are developed and deployed without sufficient transparency and accountability. This paper explores the complex landscape of ethics and bias in AI, examining how biases—both implicit and (...) explicit—can be embedded into algorithms through biased training data, flawed model assumptions, or unintended consequences of design choices. We analyze real-world case studies to illustrate the tangible impacts of biased AI systems, such as discriminatory hiring tools or racial profiling in predictive policing software. Furthermore, we discuss the limitations of current technical solutions aimed at mitigating bias, including fairness metrics and algorithmic audits, and evaluate the roles of human oversight and policy intervention. The discussion also delves into broader philosophical and societal questions: Can machines be trusted to make ethical decisions? Who is accountable when AI systems cause harm? And how do we balance innovation with responsibility? Ultimately, the goal is to advocate for a human-centered approach to AI development that emphasiz. (shrink)

The proposed system leverages Convolutional Neural Network (CNN) technologies to perform early lung cancer detection through the analysis of chest CT scan images. Designed as an AI-powered diagnostic aid, the system operates independently of human interpretation, enhancing speed and reliability in medical imaging evaluation. Built using Python-based technologies, the system utilizes TensorFlow and Keras frameworks for deep learning model development and training, supported by OpenCV for image pre-processing and visualization. The system processes CT scan slices to identify pulmonary nodules and (...) classifies them into benign or malignant categories using a trained CNN model. The detection pipeline includes grayscale normalization, image resizing, and segmentation procedures to enhance the visibility of lung tissues. The CNN model extracts spatial hierarchies of features through multiple convolutional and pooling layers, followed by fully connected layers for final classification. In cases where the system detects potential malignancy, it generates a detailed report including confidence scores, prediction labels, and scan timestamps. To maintain responsiveness and scalability, prediction requests are managed through concurrent processing threads, ensuring timely feedback even when multiple scans are evaluated in sequence. An alert throttling mechanism ensures the reduction of false positives by incorporating prediction stability checks over multiple frames or scans. (shrink)

Hand gesture recognition and speech recognition system is an innovative solution that translates hand gestures and spoken language into readable text in real-time, facilitating seamless communication for individuals with speech or hearing impairments. By leveraging advanced computer vision techniques, including OpenCV and Media Pipe for hand landmark detection and tracking, and TensorFlow-based machine learning models for precise gesture classification, the system enables effortless communication. Additionally, its integrated speech-to-text functionality allows for the transcription of spoken words, catering to diverse user needs (...) and preferences. With features like realtime processing, customizable gesture mappings, and multilingual support, this system is poised to bridge the communication gap in various settings, including personal, educational, and healthcare environments, ultimately promoting inclusivity and enhancing the quality of life for individuals with disabilities. By providing an intuitive and accessible means of communication, the system empowers users to express themselves more effectively, fostering greater independence, confidence, and social interaction. (shrink)

The integration of cloud architecture into Human Capital Management (HCM) systems has revolutionized the way organizations manage their workforce. Cloud-based HCM solutions offer enhanced scalability, flexibility, and accessibility, enabling organizations to streamline HR processes, improve data management, and foster greater employee engagement. This paper explores the impact of cloud architecture on HCM processes, highlighting key benefits such as cost-effectiveness, real-time data access, and seamless integration with other business systems. Through a comprehensive literature review and analysis of case studies from diverse (...) industries, the study demonstrates how cloud-based HCM systems optimize talent acquisition, performance management, learning and development, and workforce analytics. Additionally, the research addresses the challenges associated with transitioning to cloud-based HCM solutions, including data security, privacy concerns, and change management. Best practices for implementing cloud architecture in HCM are discussed, emphasizing the importance of strategic planning, stakeholder engagement, and continuous evaluation. The findings underscore the pivotal role of cloud architecture in enhancing organizational efficiency, agility, and competitive advantage in today’s dynamic business environment. (shrink)

Natural Language Processing (NLP) is a crucial area within artificial intelligence (AI) that focuses on the interaction between computers and human language. By enabling machines to process and understand human language, NLP bridges the gap between computers and human communication. The aim of this paper is to explore key concepts and methodologies in NLP, including text analysis, language modeling, and machine learning techniques. We also discuss challenges faced in NLP such as ambiguity, contextual understanding, and the evolution of language. This (...) paper highlights recent advancements, applications in real-world scenarios, and suggests directions for future research. (shrink)

The unprecedented growth of Large Language Models (LLMs) has transformed text generation, but maintaining the validity and dependability of their output is a still an unresolved problem. This article presents an overall framework for validating LLM output using a hybrid methodology that unites automated testing and human auditing. The approach uses fact-checking tools, semantic coherence tests, and source-based authentication to rigorously examine the accuracy, coherence, and factuality of generated output. Through the incorporation of these methods, the framework solves major shortcomings (...) in existing validation methodologies, such as hallucination and contextual drift. To illustrate pragmatic usability, the validation method is applied in a document-based QA(Question and Answers) system with Google's Gemini model. The system cross-checks LLM results against source PDF content and applies confidence scoring for marking uncertain responses. Experimental work indicates that it is possible to enhance validation accuracy by 72% by coupling retrieval-augmented verification and statistical uncertainty estimation with individual standalone methods. Adaptive learning is incorporated into the framework to improve the validation rules under error patterns so that the entire system is enhanced in terms of robustness to different domains. This work adds to the emerging body of reliable AI through the introduction of a scalable, multi-modal verification solution for LLM-based applications. The developed methods not only enhance output trustworthiness in document analysis systems but also define generalizable best practices for content verification. Real-time validation architectures and domain-specific adaptation mechanisms will be investigated in future work to further enhance the robustness of the framework in mission-critical applications. (shrink)

With the exponential growth of online social networks, now boasting over 4 billion active users, maintaining data integrity and ensuring user security has become increasingly challenging. Social media platforms face numerous security threats, including the creation of fake profiles by malicious users seeking to steal sensitive information, spread misinformation, or engage in fraudulent activities. Due to the anonymity afforded by these platforms, detecting such deceptive accounts manually is both difficult and inefficient. This project focuses on the detection of fake profiles (...) using machine learning and deep learning techniques, trained on platform-specific datasets to improve accuracy. A web-based application has been developed featuring four core modules, each dedicated to a major social media platform experiencing rapid user growth. Each module integrates a trained model to assess and classify user profiles as real or fake. Currently, the system has been implemented for Instagram, with plans to expand to other platforms. This approach aims to provide a scalable and intelligent solution to enhance user trust and safety across social media environments. (shrink)

Zero Trust (ZT) is a security model that assumes no entity, whether inside or outside the network, is trusted by default. As organizations increasingly migrate their infrastructure to the cloud, traditional perimeter-based security models prove ineffective in protecting against advanced threats. This paper explores the implementation of Zero Trust in cloud environments and its impact on enhancing security in the modern era. By focusing on identity management, network segmentation, and continuous verification, the study highlights the key principles of Zero Trust (...) and discusses its effectiveness in mitigating risks like data breaches and insider threats. The paper also examines best practices, challenges, and future trends in adopting Zero Trust within cloud ecosystems. (shrink)

The emergence of life is commonly approached from a probabilistic and biochemical perspective. However, this paper presents an alternative hypothesis: that the origin of life may be governed not solely by random chance, but by an underlying mathematical structure embedded within fundamental constants and natural proportions. This study explores the correlation between the golden ratio (φ ≈ 1.618), perfect numbers such as 6 and 28, and the gravitational acceleration constant on Earth (g ≈ 9.8 m/s²). Notably, 6 × φ yields (...) a result remarkably close to Earth’s gravitational acceleration, suggesting a non-random alignment that may carry deeper implications. The paper posits a “Theory of Necessity” — that life may arise not merely through probabilistic means, but through deterministic patterns in physical laws and mathematics. Though speculative, this hypothesis offers a compelling case for considering numerical harmony as a significant factor in the emergence of life and consciousness. (shrink)