International Journal of Reciprocal Symmetry and Theoretical Physics
International Journal of Reciprocal Symmetry and Theoretical Physics is a peer-reviewed journal, that publishes original research papers, comments, and brief reviews in all aspects of theoretical and mathematical physics, with accentuation on all emerging fields of physical sciences. Abbreviated key title: Int. j. recipr. symmetry theor. phys. For submission and other contacts: ijrstp@upright.pub
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Leveraging AI in SAP GTS for Enhanced Trade Compliance and Reciprocal Symmetry Analysis
This research investigates how SAP Global Trade Services (GTS) may improve trade compliance and reciprocal symmetry analysis in global trade processes using AI. The project explores how AI-driven solutions may enhance real-time compliance monitoring, expedite documentation and categorization, discover trade data abnormalities, and optimize reciprocal symmetry analysis between trading partners. The secondary data-based research examines trade compliance AI literature and case studies on machine learning, natural language processing, and predictive analytics. Significant results show that SAP GTS AI integration improves trade compliance accuracy, efficiency, and proactivity. AI allows real-time anomaly detection, automatic categorization, and predictive analytics for risk reduction, enhancing compliance with changing rules and international trade agreements. AI-powered reciprocal symmetry analysis identifies trade disparities, promoting fair trade. The report also notes that high-quality data, model upgrades, and data protection regulations are required. Policies propose that corporations, regulators, and legislators work together to ethically employ AI in global commerce while addressing cybersecurity and data protection issues. Businesses benefit from SAP GTS' AI integration, which ensures compliance and operational efficiency in the increasingly complicated global trade sector.
Copyright (c) 2022 International Journal of Reciprocal Symmetry and Theoretical Physics https://creativecommons.org/licenses/by-nc/4.0
Automating SAP GTS Compliance through AI-Powered Reciprocal Symmetry Models
This research automates and optimizes trade compliance operations using SAP Global Trade Services (GTS) and AI-powered reciprocal symmetry models. The paper examines how reciprocal symmetry models may increase compliance efficiency, regulatory adaptation, and risk management in global trade operations. AI model scalability and predictiveness in SAP GTS are secondary goals. This secondary data-based evaluation examines current research and case studies to determine if AI can change trade compliance systems. Significant results show that AI models boost operational efficiency by automating compliance duties, proactively recognizing hazards, and rapidly adapting to regulatory changes. These models improve complicated trade data management decision support and scalability. Data quality, system integration, and the high cost of AI implementation were noted as issues. Policy implications underline the need for explicit AI use norms in trade compliance, data security, privacy, and human monitoring to assure responsibility. The study found that incorporating reciprocal symmetry models into SAP GTS transforms trade compliance, allowing organizations to remain compliant while improving operational agility. Still, sustainable deployment requires continual policy creation and research.
Copyright (c) 2020 International Journal of Reciprocal Symmetry and Theoretical Physics https://creativecommons.org/licenses/by-nc/4.0
MLOps-Driven Solutions for Real-Time Monitoring of Obesity and Its Impact on Heart Disease Risk: Enhancing Predictive Accuracy in Healthcare
This research examines MLOps-driven real-time obesity and heart disease risk monitoring systems to improve healthcare prediction. Secondary data assesses the literature on machine learning operations (MLOps) frameworks and predictive modeling in obesity-related cardiovascular risks. Continuous deployment of predictive models allows real-time changes based on the newest patient data, improving their flexibility and accuracy. Integrating electronic health information and wearable devices improves models' capacity to give timely and individualized healthcare. However, data protection and infrastructure for continual model retraining remain issues. Policy implications imply data security and equal access to modern healthcare technology need regulatory frameworks. Transparent AI standards in therapeutic contexts will also build confidence and responsibility. This study shows that MLOps frameworks may enhance obesity and heart disease management, enabling better preventative care methods for various populations.
Copyright (c) 2019 International Journal of Reciprocal Symmetry and Theoretical Physics https://creativecommons.org/licenses/by-nc/4.0
Applying Convolutional Neural Networks for IoT Image Recognition
This research uses edge computing to overcome real-time processing issues in Internet of Things (IoT) picture identification using Convolutional Neural Networks (CNNs). The main goals are to study lightweight CNN architectures, model compression, and edge computing's impact on latency and bandwidth. The article reviews CNN literature and its use in resource-constrained IoT situations using secondary data. Significant results show that lightweight models like MobileNet and EfficientNet are essential for efficient picture identification without losing accuracy, while edge computing decreases latency and improves real-time decision-making. Hardware accelerators help install complicated CNN models on IoT devices. Limitations include edge infrastructure reliance and data privacy issues. The report stresses the need for solid data protection regulations and energy-efficient hardware research. Policymakers should establish IoT data protection standards and accessible edge infrastructure to ensure fair deployment of CNN-based image recognition systems across varied geographies. This study helps explain CNN-IoT synergy, laying the groundwork for real-time image processing advances.
Code Refactoring Strategies for Enhancing Robotics Software Maintenance
Code refactoring solutions for robotics software maintenance and optimization are examined in this paper. The critical goal is finding refactoring methods that increase code maintainability, performance, and real-time restrictions in robotics applications. Using secondary data, the research synthesizes the literature on robotics software restructuring, performance improvement, and maintenance difficulties. Research shows modular design, readability enhancements, and algorithmic changes increase program maintainability and performance. More explicit code, better debugging, and enhanced real-time performance are advantages. The report admits constraints, including longer development times and more significant bug risks. According to policy, structured refactoring, automated testing, and industry standards may reduce risks and improve maintenance. By combining these tactics, developers may keep robotics systems resilient, adaptive, and ready for new technology.
Copyright (c) 2021 International Journal of Reciprocal Symmetry and Theoretical Physics https://creativecommons.org/licenses/by-nc/4.0
AI-Enhanced Reciprocal Symmetry in Nanoparticle-Thermoplastic Compounding: Towards a Digital Transformation in Materials Science
This study explores the transformative potential of artificial intelligence (AI) in nanoparticle-thermoplastic compounding, focusing on enhancing reciprocal symmetry to achieve superior material properties. The principal objective is to investigate how AI algorithms can optimize nanoparticle dispersion within thermoplastic matrices, leading to improved performance and broader application potential. Utilizing a secondary data-based methodology, the research involves a comprehensive review and analysis of existing literature, including peer-reviewed journal articles, industry reports, and relevant case studies. Key findings highlight that AI significantly enhances predictive accuracy and optimization capabilities in nanocompounding. Machine learning and deep learning models accurately predict nanoparticle behavior, ensuring uniform dispersion and consistent material properties. Practical applications in the automotive, healthcare, and consumer electronics industries demonstrate tangible benefits, including improved material strength, biocompatibility, and thermal and electrical conductivity. AI-driven processes also contribute to sustainability by minimizing waste and reducing energy consumption. Technical implications underscore the role of AI in driving digital transformation within materials science. AI facilitates data-driven decision-making, automation, and innovation, leading to more efficient and accurate compounding processes. Future research should focus on integrating AI with IoT and smart manufacturing systems, developing more sophisticated algorithms, and promoting collaborative research and open data initiatives. This study concludes that AI-enhanced reciprocal symmetry in nanoparticle-thermoplastic compounding holds significant promise for advancing materials science and various industry applications.
Copyright (c) 2024 International Journal of Reciprocal Symmetry and Theoretical Physics https://creativecommons.org/licenses/by-nc/4.0
Optimizing Web Performance: Front End Development Strategies for the Aviation Sector
This study investigates front end development strategies to optimize web performance for airline websites, aiming to enhance user experience and operational efficiency. The primary objectives include identifying critical performance metrics and implementing effective optimization techniques. The methodology involved analyzing key metrics such as page load time, TTFB, FCP, LCP, CLS, TBT, and TTI, followed by the deployment of best practices like minimizing HTTP requests, optimizing images, leveraging lazy loading, and using content delivery networks (CDNs). Continuous monitoring and user feedback were integral to the iterative improvement process. Principal findings indicate that these strategies significantly improve web performance, resulting in faster load times, reduced latency, and enhanced user satisfaction. Ensuring HTTPS implementation and robust security measures further bolstered performance and trust. The study concludes that a holistic approach to web performance optimization can provide airlines with a competitive edge, fostering better customer experiences and driving business success.
Copyright (c) 2017 International Journal of Reciprocal Symmetry and Theoretical Physics https://creativecommons.org/licenses/by-nc/4.0
Reciprocal Symmetry and the Unified Theory of Elementary Particles: Bridging Quantum Mechanics and Relativity
The search for a unifying concept that might reconcile relativity and quantum physics has led to the investigation of reciprocal symmetry. This work aims to formulate a unified theory of fundamental particles and space-time by clarifying reciprocal links between quantum and space-time symmetries. This study's technique entails a thorough analysis and synthesis of the body of knowledge regarding symmetry in relativity and quantum physics. Examining theoretical frameworks, mathematical formalisms, and experimental discoveries, the research finds mutual links and shared symmetries across conventional disciplinary boundaries. The study's main conclusions emphasize that symmetry is essential in forming physical interactions and rules. The structure of relativistic space-time geometry and quantum events is revealed to be unified by symmetry. The findings have consequences for policy in the areas of experimental facility investments, multidisciplinary collaboration promotion, and the creation of cutting-edge technical tools for theoretical physics research.
Emergence of Reciprocal Symmetry in String Theory: Towards a Unified Framework of Fundamental Forces
The origin of reciprocal symmetry in string theory and its consequences for a unified theory of fundamental forces in theoretical physics are examined in this paper. Characterizing reciprocal symmetry in string theory, exploring its relationship to fundamental forces, and reviewing its possible role in reaching grand unification are the main goals of this research. This study uses a review-based methodology to examine the literature and secondary data on string theory's reciprocal symmetry. Peer-reviewed books, journals, and conference proceedings are included in the literature review, which focuses on essential ideas such as dualities, symmetries, and symmetry breaking in string-theoretic frameworks. The paper sheds light on the intricate relationships between forces and symmetries in string theory and explains how reciprocal symmetry unites various physical phenomena under a single mathematical framework. The paper highlights the necessity for ongoing investment in theoretical and experimental physics by identifying theoretical constraints and difficulties in studying reciprocal symmetry. The findings of this study highlight the value of funding STEM education, encouraging interdisciplinary collaborations, and supporting theoretical physics to develop future researchers who can tackle the challenges of string theory and move closer to unifying the fundamental forces.
Integrating AI and Reciprocal Symmetry in Financial Management: A Pathway to Enhanced Decision-Making
Using AI and reciprocal symmetry to improve financial management decision-making is revolutionary. The effectiveness of integrating AI technology within reciprocal symmetry principles in financial decision-making is examined in this study. This study examines how AI can evaluate complex financial data and find patterns in interconnected financial networks. The study also examines reciprocal symmetry and its effects on holistic and context-aware finance decision-making. Scholar articles, research papers, and industry reports were reviewed to synthesize knowledge on AI and reciprocal symmetry in financial management. The study shows that incorporating AI into reciprocal symmetry improves decision-making by offering holistic insights into market dynamics, boosting risk assessment and mitigation measures, and enabling adaptive responses to market complexity. Addressing data quality, AI algorithm biases, and ethical issues is essential for responsible AI deployment and ethical and equitable financial regulations. This study shows how AI and reciprocal symmetry might improve financial decision-making and innovation.
Copyright (c) 2018 International Journal of Reciprocal Symmetry and Theoretical Physics https://creativecommons.org/licenses/by-nc/4.0
Role-Based Access Control in SAS Programming: Enhancing Security and Authorization
To improve security and authorization protocols, this study investigates the application of Role-Based Access Control (RBAC) in SAS (Statistical Analysis System) programming environments. Determining roles, allocating permissions, enforcing access controls, and assessing the effect of RBAC on security were the study's primary goals. The literature that has already been written, case studies, and best practices around RBAC deployment in SAS systems were examined using a secondary data-based review methodology. The main conclusions emphasize how well RBAC works to increase control granularity, streamline administrative duties, facilitate scalability, bolster auditability and compliance, and bolster overall security postures. Nonetheless, restrictions were noted, highlighting the necessity of precise guidelines and continuous audits. These included role complexity and administrative burden. The significance of creating precise role-based policies and carrying out frequent audits to maximize RBAC effectiveness are among the policy implications. This study emphasizes that RBAC is essential to contemporary data analytics workflows, supporting safe and adequate access control methods in SAS programming environments that comply with legal requirements and organizational goals.
AI-enabled Decision Support Systems and Reciprocal Symmetry: Empowering Managers for Better Business Outcomes
To empower managers and improve business outcomes in organizations, this paper explores the revolutionary potential of AI-enabled Decision Support Systems (DSS) combined with reciprocal symmetry. The study aims to investigate how integrating AI improves managerial skills, encourages cooperative decision-making through reciprocal symmetry, and spurs creativity and operational efficiency. The methodology includes a thorough analysis of secondary data sources and published literature on artificial Intelligence (AI) in reciprocal symmetry and decision assistance. To summarize the main conclusions and insights, scholarly articles, peer-reviewed journal articles, and pertinent reports were examined and evaluated. The main findings emphasize how Artificial Intelligence (AI) can improve managerial decision-making accuracy, promote cooperative cultures through reciprocal symmetry, and stimulate innovation and operational efficiency inside businesses. The report also outlines policy implications for ethical issues, data protection, and organizational preparedness for effective AI integration. In today's fast-paced business world, this study emphasizes how crucial it is to strategically use AI technology within a framework of mutually beneficial interactions to empower managers, promote innovation, and achieve sustainable success. The results provide insightful information that may be used to establish corporate plans and policies for using AI to improve business outcomes.
Massive MIMO: Revolutionizing Wireless Communication through Massive Antenna Arrays and Beamforming
Massive MIMO technology has become a paradigm-shifting advancement in wireless communication networks by utilizing large antenna arrays and advanced beamforming algorithms. With an emphasis on Massive MIMO's scalability, coverage enhancement, spectrum efficiency, system capacity boost, mobility and handover performance optimization, interference management, and coexistence capabilities, this study intends to investigate the potential of Massive MIMO in revolutionizing wireless communication. Using extensive analysis of extant literature and practical performance assessments, the research pinpoints crucial research obstacles and policy ramifications for the triumphant implementation and acceptance of Massive MIMO technology. Essential discoveries include the excellent rise in spectrum efficiency and system capacity, the scalability and coverage enhancement provided by Massive MIMO systems, and the efficacy of coexistence and interference management strategies. To fully realize the transformative potential of Massive MIMO technology and advance digital connectivity for all, policy implications such as spectrum allocation and regulation, support for research and development, infrastructure deployment and investment, standardization, interoperability, and digital inclusion are essential.
High-Performance VLSI Architectures for Artificial Intelligence and Machine Learning Applications
Artificial intelligence (AI) and machine learning (ML) applications are accelerated by high-performance VLSI architectures, which allow for real-time inference, analysis, and decision-making across a wide range of disciplines. The design, development, and implementation of VLSI architectures for AI and ML applications are examined in this paper, with an emphasis on scalability, efficiency, and practicality. The study's primary goals are to examine architectural paradigms, optimization strategies, energy-efficient design concepts, performance evaluation approaches, and practical uses of high-performance VLSI architectures for AI and ML. A thorough analysis of the body of research, case studies, and policy implications about VLSI design for AI and ML applications are all part of the methodology. Principal discoveries emphasize the variety of architectural paradigms, optimization strategies, and practical uses of high-performance VLSI architectures, along with their implementation difficulties and policy ramifications. The significance of ethical deliberations, adherence to regulations, and international cooperation in guaranteeing the conscientious and fair application of artificial intelligence and machine learning is highlighted by policy ramifications. By offering insights into the design, optimization, deployment, and policy implications of high-performance VLSI architectures for AI and ML applications, this study advances our collective understanding of these technologies and the field of AI-driven technologies.
Next-Generation Electric Machines: Integration of Power Electronics and Machine Design for Optimal Performance
Power electronics and machine design are changing electric machines, providing unprecedented prospects for next-generation system performance, efficiency, and dependability. This paper examines the principles, difficulties, and prospects of power electronics-machine design integration for optimal performance. The paper examines advanced optimization methodologies, case studies, and real-world applications from various industries to determine policy implications for integrated electric machine system adoption and implementation. This study analyzes the transformational potential of integrated electric machine systems using a comprehensive analysis of literature, case studies, and industry reports. Fundamental discoveries include synergistic integration between power electronics and machine design, sophisticated optimization methodologies to improve performance and efficiency, and broad applications of next-generation electric machines across industries. Policy implications emphasize the need for investment in R&D, standardization, environmental sustainability, and workforce development to overcome challenges and realize integrated electric machine systems' full potential in driving technological progress toward a cleaner, more sustainable energy future.
Emerging Trends in Smart Grid Technologies: An Overview of Future Power Systems
This article provides a comprehensive exploration of the evolving landscape of power systems, focusing on the transformative influence of emerging trends in smart grid technologies. Investigating the integration of renewable energy sources, the study showcases how advanced forecasting models, energy storage solutions, and demand-side management strategies are shaping a sustainable and resilient power generation future. The critical role of communications infrastructure is highlighted, emphasizing the necessity of robust networks and the evolution towards IoT. A paradigm shift towards consumer empowerment is elucidated, revealing how smart grids grant individuals unprecedented control over energy consumption. Despite challenges such as financial investments and cybersecurity concerns, the findings underscore the opportunities for innovation, efficiency, and positive transformation in the power sector. This research advocates for continued collaborative efforts to propel smart grids towards a future marked by resilience, efficiency, and sustainability in power systems.
Copyright (c) 2016 International Journal of Reciprocal Symmetry and Theoretical Physics https://creativecommons.org/licenses/by-nc/4.0
Indie Game Alchemy: Crafting Success with C# and Unity's Dynamic Partnership
This piece digs into the mutually beneficial relationship between the C# programming language and the Unity game creation engine, examining the vital role that both components have played in transforming the development process of independent video games. The powerful pairing of Unity and C# has enabled developers to create engaging gaming experiences by combining the variety and accessibility of Unity with the elegance and efficiency of C#. The evolution of Unity, the influence of the Unity Asset Store, and the general-purpose capabilities of C# are all topics that are discussed in this article. This article delves deeper into the seamless integration of C# with Unity, highlighting the benefits of event-driven programming. The essay addresses the issues that developers are facing and offers insights into the future trends of this powerful alliance. It does this by highlighting the concrete impact of this collaboration through case studies of successful independent video games. The article says that the synergy between C# and Unity will continue to be a cornerstone, fueling creativity and attracting audiences worldwide as the development of independent video games continues to progress.
Pushing the Boundaries: Advanced Game Development in Unity
Unity is a game engine that has revolutionized the game production industry because of its flexibility and widespread adoption. This engine provides a broad set of tools and capabilities, making it available to developers with varying degrees of ability and experience. At its foundation, Unity gives artists the ability to bring their ideas to life in the form of experiences that are both interactive and engaging for users. This detailed article explores Unity game production. This article gives game developers an overview of the fundamentals and advanced strategies to make complex and enjoyable games. Optimizing game performance, building complicated AI systems, and using Unity's latest features are covered in the article. Readers will learn how to create beautiful, cross-platform games. Additionally, the paper emphasizes the necessity of remaining current with developing technology and how Unity can create immersive experiences. Modern game creation relies on networking and multiplayer capability. By the end of this article, readers will comprehend Unity's advanced game development landscape and be inspired to create cutting-edge games that capture gamers and push the gaming industry's limits.
Copyright (c) 2017 Parikshith Reddy Baddam https://creativecommons.org/licenses/by-nc/4.0
Networking Alchemy: Demystifying the Magic behind Seamless Digital Connectivity
Computer networking is essential to the operation of the internet and other forms of communication and data transfer made possible in today's digitally interconnected world. This article aims to unravel the complexities of computer networking in a clear and approachable manner. This in-depth article dives into the underlying ideas behind computer networking, including subjects such as the OSI model, TCP/IP protocols, network topologies, and routing; it elucidates the functions of various network equipment, including routers, switches, and firewalls, and describes how these devices work together to ensure the uninterrupted delivery of data. In addition, this study investigates contemporary developments and technologies in the realm of networking, including virtualization, cloud computing, and the Internet of Things (IoT). In this article, the readers will learn about best practices for network security, troubleshooting approaches, and performance optimization measures. Whether we're a novice looking to grasp the basics or a seasoned professional seeking a refresher, this article offers valuable insights to help you navigate the intricacies of computer networking. It demystifies the networking world with concise explanations and practical examples, making it accessible to a wide range of readers.
Copyright (c) 2017 Swathi Kaluvakuri, Karu Lal https://creativecommons.org/licenses/by-nc/4.0
Python Programming Language for Data-Driven Web Applications
Selecting an appropriate programming language to carry out any necessary job associated with programming or any other pertinent field is the most essential duty for anyone just starting in the field of computers. Selecting a programming language approachable for novices is of the utmost significance, as this decision is a critical stepping stone to becoming a programmer. If we choose a complex programming language, it may cause us to lose interest in computer programming. This article focuses on the characteristics of Python, as well as its applications, benefits, drawbacks, and other aspects. It also briefly discusses object-oriented principles that may be easily implemented using Python. This may assist the reader in understanding why they should choose Python as their first programming language if they are starting.
Internet of Things (IoT) Technology for Use as Part of the Development of Smart Home Systems
The Internet of Things (IoT) is a relatively new technology gradually advancing our world. With the Internet of Things, it is possible to conceive of a connected world. One example is a "Smart Home" built on the Internet. In a Smart household environment enabled by IoT, various objects, including lighting, household appliances, laptops, security cameras, and so on, are all connected to the Internet. This allows the user to monitor and control things regardless of the time or location in which they are located. This paper provides an overview of the Frugal Labs IoT Platform (FLIP), which may be used to construct smart homes that IoT enables. This paper explains the FLIP architecture and analyzes the functionalities of Smart Homes as well as the applications of Smart Homes. Finally, this paper proposes a system for implementing Smart Home services utilizing FLIP. The proposed method provided in this study is used to monitor and control the atmosphere of a smart home.
Copyright (c) 2018 Harshith Desamsetti https://creativecommons.org/licenses/by-nc/4.0
Making the Cloud Adoption Decisions: Gaining Advantages from Taking an Integrated Approach
This article helps IT decision-makers break down strategic factors to plan cloud and managed services transformations. It has elements of strategic, financial, architecture, security, process, and people. Cloud adoption topics emerge from the review. Cloud adoption has mainly been studied from the innovative technology standpoint utilizing the Technology-Organization-Environment framework. Other popular approaches include the transaction cost theory-driven economic/cost perspective and multicriteria decision framework. The report begins with a concentrated cloud adoption literature review. The literature is then carefully categorized by view (commercial versus technical), dominant theory, and adoption factors. Two existing frameworks are also evaluated for merits and weaknesses. Finally, a brief checklist is created from the cumulative findings. The study organizes reviewed publications' recurring themes into four key sections. The research also includes frequently asked questions in each category to assist firms in assessing their cloud readiness. Thus, the essay blends multiple views and provides enterprises with a straightforward, holistic checklist to consider cloud business implications.
Copyright (c) 2016 Upendar Rao Thaduri, Venkata Koteswara Rao Ballamudi, Sreekanth Dekkati, Mounika Mandapuram https://creativecommons.org/licenses/by-nc/4.0
How Distributed Cloud Computing Works: An Overview
The cloud computing model is generalized by distributed computing, which allows data and applications to be positioned, processed, and served from geographically dispersed locations to meet performance, redundancy, and compliance requirements. The classic cloud computing model provides users who wish to refrain from constructing, purchasing, or managing their information technology infrastructure with on-demand, metered access to computing resources such as storage, servers, databases, and applications. Public cloud service providers maintain and operate large server farms whose resources are shared amongst users. Users benefit from the isolation and security of their data thanks to virtualization techniques implemented in these server farms. Site redundancy that spans regions allows for recovery during outages or natural catastrophes. In addition, cloud users are not required to be involved in the monitoring or management processes necessary to keep the cloud operational. This study investigates whether distributed cloud service providers guarantee end-to-end management for the best data placement, computing processes, and network interconnections following the above specifications. And when seen from the perspective of someone who uses the cloud, the study offers a unified answer.
Copyright (c) 2020 Samuel Koehler https://creativecommons.org/licenses/by-nc/4.0
A Review of Hosting Enterprise SaaS with IaC on Multi-cloud Platforms
This paper explores the concepts of Enterprise Software as a Service (SaaS), the reasons for hosting a SaaS solution on a multi-cloud platform, and the benefit of using Infrastructure as Code (IaC) on a multi-cloud for quick provisioning. The paper also details the various challenges faced while adopting a multi-cloud solution. In conclusion, the paper recommends using multi-cloud and IaC to host all Enterprise SaaS solutions. DOI: https://doi.org/10.18034/ijrsps.v7.72
Copyright (c) 2020 Sandesh Achar, Nathaniel Louis Tisuela https://creativecommons.org/licenses/by-nc/4.0
Requirement of Cloud Analytics and Distributed Cloud Computing: An Initial Overview
A better and more streamlined user experience is the result of the research demonstrating how cloud analytics tools and software are particularly effective for processing vast data sets, producing insights in easily digestible formats on demand, and improving the user interface. Cloud computing is a large-scale information technology solution that can customize and deliver a new kind of environment based on information technology to everyday users, in particular to IT and computing systems users. Cloud computing was developed by Amazon Web Services (AWS) and Microsoft Azure. The use of cloud computing comes with a multitude of features and aspects, including internet backup. It comes with just-in-time delivery of standardized storage process, management, and infrastructure, as a measurable service, on a 'Pay-as-you-go' type, and is therefore widely accessible in various organizations and institutions. Cloud computing and its load balancing are essential features that must be taken care of to maintain a healthy Information Management system. In a condensed form, this paper discusses a significant number of topics that are connected to the given subject. DOI: https://doi.org/10.18034/ijrsps.v2.70
Copyright (c) 2015 Sandesh Achar https://creativecommons.org/licenses/by-nc/4.0
Data-driven Approach to Enhance Roster of Operations: A Review
When it comes to today's business environment, especially in the operations-centered industry, the most important goal is to improve the roster of activities and serve those operations in the most efficient manner possible. Powered by data Modern optimization algorithms in combination with models based on production factors have been proved to be useful in the manufacturing business for optimizing production schedules and increasing profitability. We have constructed time and cost models based on real-world data that we have collected. Make use of the information provided to determine the most effective solution to the Job-Shop Scheduling Problem utilizing three algorithms: particle filter, particle swarm optimization, and the genetic algorithm (if applicable). When we want to create operational rosters that are based on a combination of time and cost optimization, the method comes in handy.
Copyright (c) 2020 Md Saikat Hosen, Harshini Priya Adusumalli, Mahesh Babu Pasupuleti https://creativecommons.org/licenses/by-nc/4.0
Expansion of Machine Learning Employment in Engineering Learning: A Review of Selected Literature
Articles were summarized and analyzed by the author based on the year of publication and the context in which the article was published, among other factors. The purpose of this study is to determine the progress made in the implementation of machine learning in a variety of engineering fields. The research approach employed was a literature review, and secondary data was gathered from renowned international journals that were indexed by Google Scholar during the process. As a result of the findings, machine learning has been widely implemented in engineering education across fourteen domains, with one of the most significant being Prediction Student Academic Performance, which has shown constant progress from 2013 to 2018. Furthermore, the total number of engineering majors who are implementing machine learning is thirteen majors in total. According to the researchers' expectations, this research will serve as an illustration, reference, and consideration for technicians in engineering education to pay greater attention to, and it will be applicable in schools, universities, and other engineering institutions throughout Indonesia.
Copyright (c) 2019 Harshini Priya Adusumalli https://creativecommons.org/licenses/by-nc/4.0
Word Embedding with ConvNet-Bi Directional LSTM Techniques: A Review of Related Literature
The exponential growth of social evaluations of services has led many researchers to focus on emotion analysis. Having so much data helps analyze end-user behavior to improve QoS. Text categorization is a prominent language processing research area that organizes the disorganized text into useable categories. The LSTM and CNN models are widely employed in text-based categorization applications and produce reliable results. By contrast, LSTM-based models gain long-term connections between text sequences and so are better suitable for text classification. In this case, the hybrid approach may memorize classification, slowing down the training process. This work proposes an optimal attention-focused BiLSTM and ConvNet model. The suggested model is trained on two independent datasets to validate its performance. The proposed attention-based model outperformed previous deep learning algorithms. Compared to existing machine learning methods, the proposed approach is more accurate.
Copyright (c) 2018 Mahesh Babu Pasupuleti, Ruhul Amin https://creativecommons.org/licenses/by-nc/4.0
Mobile Application Development through Design-based Investigation
Designing culturally responsive mobile learning courses will be made easier as a result of this study. Design-based research will be made easier as a result of this study. The outcomes of this study were based on the observations of a small sample of potential consumers. The objective of this paper is to demonstrate the development and testing of an innovative mobile application through the use of design-based research methods and techniques. This study describes the process of digitizing existing printed course material using design-based research, where design, research, and practice were all applied simultaneously. One session each from BSc Nursing, Pharmacy, and Medical Laboratory Sciences were chosen for this transition. The major research question was formulated in the first step. OUSL MLearn, a mobile learning application, was conceived and built-in phase 2. In the third phase, this application was evaluated by five groups of stakeholders: content experts to validate content, educational technologists to align technical and pedagogical features, novice users to assess overall effectiveness, developers to assess ease of use, and researchers to assess impact. These stakeholders were closely involved throughout the four-month project. The outcomes of this phase were analyzed and used to improve the product. The findings have implications for the design of interactive mobile applications that are culturally responsive. It was discovered that the built mobile application was easy to use, visually appealing, and pedagogically beneficial for its target audience. Optimization, development time, technical and organizational concerns, the workload of academics, and production expenses, on the other hand, were regarded as the most significant obstacles.
Copyright (c) 2017 Harshini Priya Adusumalli https://creativecommons.org/licenses/by-nc/4.0
Data Science: The Sexiest Job in this Century
Data science is the study of huge amounts of data in order to uncover insights that may be used to assist companies in making strategic decisions. There are several routes to a job in data science; the majority, but not all, entail a little math, a little science, and a great deal of interest in the subject matter of data. New data scientists must be inquisitive, critical, and persuasive in their reasoning. The research focuses on why data science is regarded as the sexiest job in the twenty-first century, as seen by the high compensation paid to qualified people.
Copyright (c) 2015 Mahesh Babu Pasupuleti https://creativecommons.org/licenses/by-nc/4.0