Srinivas Chippagiri

The popularity of web apps and other forms of online application development has skyrocketed in the last few years. It is crucial to choose the optimal technology or framework,especially given the proliferation of options. This survey explores three major paradigms: Ruby on Rails, Python-based frameworks and cloud-based architectures. Ruby on Rails is focused on the MVC model and consists of an ideal structure ofworking and the philosophy of convention over configuration increasing productivity along with focusing onsuch values as standardization of work. Two widely used Python-based frameworks are Django and Flask; these havethe options for extensive flexibility, scalability and libraries to support virtually any kind of application that one might wantto build. Cloud computing, with its layers and service models, brings great scalability, cost efficiency, and a rich resourcepool to deployment. Additionally, some research space has been addressed that needs to be tackled related to the improvement of frameworks, flexibility and their scalability as well as long-term sustainment of the systems; these areas pose a need for applying inter-disciplinary solutions incorporating cutting-edge technologies for building more reliable and efficient web applications.

Abstract: The field of sentiment analysis is a crucial aspect of natural language processing (NPL)and is essential in discovering the emotional undertones within the text data and, hence, capturingpublic sentiments over a variety of issues. In this regard, this study suggests a deep learningtechnique for sentiment categorization on a Twitter dataset that is based on Long Short-TermMemory (LSTM) networks. Preprocessing is done comprehensively, feature extraction is donethrough a bag of words method, and 80-20 data is split using training and testing. The experimentalfindings demonstrate that the LSTM model outperforms the conventional models, such as SVM andNaïve Bayes, with an F1-score of 99.46%, accuracy of 99.13%, precision of 99.45%, and recall of99.25%. Additionally, AUC-ROC and PR curves validate the model’s effectiveness. Although, itperforms well the model consumes heavy computational resources and longer training time. Insummary, the results show that deep learning performs well in sentiment analysis and can be usedto social media monitoring, customer feedback evaluation, market sentiment analysis, etc.

Effective task scheduling in cloud computing is crucial for optimizing systemperformance and resource utilization. Traditional scheduling methods often struggle to adapt to thedynamic and complex nature of cloud environments, where workloads, resource availability, andtask requirements constantly change. Swarm intelligence-based optimization algorithms, such asParticle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony(ABC), offer a promising solution by mimicking natural processes to explore large search spacesefficiently. These algorithms are effective in balancing multiple objectives, including minimizingexecution time, reducing energy consumption, and ensuring fairness in resource allocation. Theyalso enhance system scalability, which is vital for modern cloud infrastructures. However,challenges remain, including slow convergence speeds, complex parameter tuning, and integrationwith existing cloud frameworks. Addressing these issues will be essential for the practicalimplementation of swarm intelligence in cloud task scheduling, helping to improve resourcemanagement and overall system performance

Recent innovations in cloud computing have prompted a sea change away from monolithic systems to cloud-native architecture, enablingdevelopers to build adaptable, scalable, and modularapplications. Key technologies such as serverless computing, containerization, and microservices architecture are used in cloud-native development, enhancingoperational efficiency, flexibility, and the deployment speed ofapplicationsin dynamic business environments. While these technologies allow organizationsto build applications with improvedadaptability, scalability and resilience, they also introduce challenges,including managing complex service dependencies, diagnosing failures, optimizingperformance, and ensuring security in highly distributed systems. Moreover, managing stateful services and ensuring data consistency in the cloud environment can be particularly challenging. In this paper,the fundamental principles of cloud-native developmentare explored with focuson resilience, scalability, and elasticity while addressing the challenges faced by developers. Further, best practices like continuous integration and deployment (CI/CD), infrastructure as code (IaC), and security-focused DevSecOps practicesare emphasized. Furthermore, a reviewofessential tools and frameworks, such as Kubernetes, Prometheus, and AWS CloudWatch, that assist in orchestrating, monitoring, and optimizingcloud-native systemsis provided. Theinsights provided aimto guide organizationsin adopting cloud-native technologies to build secure, high-performance applications

Financial markets are always volatile and do not depend on a single macro-economic indicator or economic data but on a whole range of factors, primarily events related to government, geopolitical issues, or corporate earnings, as well as investor sentiment. Unlike traditional quantitative models like time series and econometric models, financial text data is very complex, and most of these models are not suitable for capturing such complexities. Large Language Mod- els (LLMs) are a game changer that has brought forth the effective use of NLP techniques to extract meaning from financial news, earnings reports, and social media sentiments. In this study, we test the efficacy of LLMs (Llama3, Gemma2, RoBERTa) in performing financial segment evaluation and financial market prediction. To improve sentiment classification accuracy, the structured methodology employed consists of data preprocessing, tokenization, and fine- tuning using QLoRA. According to experimental results, Llama3 performs better than other models, achieving an accuracy of 86.1%, RoBERTa attains 85.9%, and Gemma2 gets 84.4%. The study demonstrates that LLMs can effectively capture the sentiment-based market movements however, the challenge lies in the interpretability of the model, removal of the model bias, and performing inference in real time. The study findings further indicate that LLMs look very promising to use in financial forecasting, outperforming conventional models on predicting financial outcomes.

The combination of creativity and innovation is something that has always seemed to be stamped exclusively for humans — until recently, with the advances in Artificial General Intelligence (AGI). In this paper, a novel hybrid AI model involving combining Generative Adversarial Networks (GANs) and Efficient- NetB3 for increasing machine-driven creativity is presented. The model generates diverse artistic samples using GAN-based data augmentation, and can still per- form accurate classification of artistic styles using EfficientNetB3. The approach was trained on the WikiArt dataset of 80,020 painted digitizations over 27 artistic styles. The performance of the hybrid model is experimentally shown to achieve a classification accuracy of 92.85% and exceeds the performance of existing AI- driven creative systems. The work presented in this research suggests that AI may be used to generate original artistic ideas, and it is joined by the larger discursive project surrounding AGI and creativity. Future work includes applying machine-generated creativity to other domains such as music, literature, and design, as well as assessing the novelty of machine-generated creativity.

The Payment Card Industry Data Security Standard (PCI DSS) has emerged as a pivotal framework in securing payment card transactions across global financial systems. This article presents a comprehensive analysis of PCI DSS, examining its evolution from disparate security programs to a unified standard, technical framework, and implementation challenges across different merchant levels. The article evaluates the effectiveness of the compliance validation mechanisms, including self-assessment questionnaires and third-party assessments, while analyzing the standard's incorporation into state legislation and its legal implications. This article reveals a complex relationship between compliance validation and actual security effectiveness by examining documented security breaches and industry responses. The findings indicate significant gaps between formal compliance and real-world security outcomes, highlighting the need for a more dynamic approach to payment card security. This article contributes to the ongoing discourse on payment security standards by identifying key challenges in implementation and suggesting potential areas for improvement in future iterations of the standard.

Software design patterns have become integral to modern software engineering, offering reusable solutions to common design challenges. These patterns simplify development, promote scalability, and improve system maintainability by providing established frameworks for solving recurring problems. This paper presents a comprehensive review of recent advancements in software design patterns, covering research published between 2018 and 2024. It examines the classification of design patterns into creational, structural, and behavioral categories, highlighting their applications in emerging technologies such as artificial intelligence (AI)-based systems, microservice architectures, and container orchestration frameworks. The review explores practical implementations, evaluates the effectiveness of patterns in improving performance and reliability, and identifies challenges such as pattern misuse, detection difficulties, and anti-pattern occurrences. The study also investigates ethical considerations in design pattern adoption and discusses recent trends like AI-assisted pattern detection tools and security-driven patterns. Findings emphasize the transformative role of design patterns in addressing modern software development needs while underscoring gaps in automation and ethical design practices. This paper concludes with suggestions for future research directions, including improved frameworks for reusability, automated pattern recognition systems, and ethical design frameworks to address contemporary challenges in software engineering.