American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS)
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Machine Learning-Based Detection of Fake Product Reviews and News Articles
Full text linkWith the proliferation of online platforms, detecting fake content such as fake reviews and fake news has become a critical challenge for ensuring the authenticity and reliability of digital information. This paper presents a comprehensive survey of machine learning (ML) techniques and models applied to fake review and fake news detection. By leveraging advanced Natural Language Processing (NLP) methods and hybrid machine learning approaches, the paper evaluates various algorithms including Support Vector Machines (SVM), Random Forests, Long Short-Term Memory (LSTM) networks, and ensemble models for their performance in detecting deceptive content. Key metrics such as accuracy, precision, recall, and F1-Score are analyzed across multiple datasets to determine the effectiveness and robustness of these approaches. Additionally, this study explores domain-specific challenges, including the handling of imbalanced datasets, linguistic nuances, and real-time detection requirements. The paper concludes by outlining future directions, emphasizing the need for enhanced models capable of addressing evolving deception techniques and integrating contextual factors for more accurate predictions
Creating a Multi-tier Architecture for Web Applications: Design and Implementation
Full text linkIn today\u27s world, where web applications are becoming more complex and diverse, the need for a layered architecture is particularly relevant. This architecture offers flexibility, scalability and ease of management, which is extremely important in the context of rapidly increasing requirements for information systems. Designing and implementing effective architectural solutions is becoming a crucial factor for the successful development and operation of web applications. Developers often face the limitations of a monolithic architecture, where every change in the code can unpredictably affect the entire system. This leads to difficulties in project management, difficulties in adding new features, and problems scaling the application. As a result, the risk of system failures increases and the rate of implementation of new features decreases. The study shows how a layered architecture can significantly reduce the dependency between system components, improve testability, and simplify the implementation of changes
Enhancing Manufacturing Efficiency through the Integration of RPA and Power Automate with Camstar MES
Full text linkThe integration of Robotic Process Automation (RPA) and Microsoft Power Automate with Siemens’ Camstar Manufacturing Execution System (MES) is transforming manufacturing workflows by streamlining operations, enhancing productivity, and reducing operational costs. This study investigates the impact of this integration on manufacturing efficiency, cost savings, and scalability. A mixed-methods approach is employed, combining qualitative case studies and quantitative performance metrics analysis from various industries. The research demonstrates that the integration of RPA and Power Automate with Camstar MES improves data accuracy, reduces manual intervention, and accelerates production processes. Results from case studies indicate significant cost savings, enhanced system scalability, and improved decision-making due to real-time data analytics. While the integration presents challenges, such as system compatibility and employee training, the benefits of streamlined workflows and operational agility outweigh these obstacles. This paper concludes with recommendations for manufacturers seeking to adopt automation technologies, emphasizing the need for careful planning, stakeholder engagement, and continuous monitoring to ensure successful implementation. By adopting RPA and Power Automate, manufacturers can achieve a more agile, efficient, and cost-effective production environment
Assessment of Background Radiations in Abuja, Federal Capital Territory, Nigeria
Full text linkBackground radiation is everywhere and investigating the amount present in the background, surroundings and environment is essential to research and public health concerns. Humans experience it daily due to both natural and artificial incidents. These incidents include cosmic radiation, internal radiation, terrestrial radiation, nuclear events and medical operations. This research covered the investigation of two area councils, Gwagwalada and Kwali Area Councils in Abuja, Federal Capital Territory, Nigeria. Field exploration and data sampling dose rate were conducted at 11 locations which are the University of Abuja Mini Campus (8.9530N, 7.0730E), Gwagwalada Market (8.9417°N, 7.0775°E), Phase III Dumpsite (8.9637°N, 7.0646°E), Landfill @ Sharia Court (8.96175°N, 7.0816°E), University of Abuja Mini Campus- Boys Hostel (8.9453°N, 7.0703°E), Old Kutunku (8.9302°N, 7.0503°E), Kwali (8.8401°N, 7.0525°E), Yangoji (8.8208°N, 7.0341°E), Tunga Sarki (8.82079°N, 7.03408°E), Tongan Sanki Health Post (8.8249°N, 6.9484°E) and Tunga Galadima (8.8079°N, 6.9209°E). The average dose rate of the overall natural background radiations in Gwagwalada and Kwali Area Councils was 0.32±0.09µSv/hr, even though in places like Yangoji (0.42±0.11µSv/hr) and Tunga Sarki (0.45±0.13µSv/hr) which are in Kwali Area Council, have higher average dose rate than the average overall for the two area councils. This dose rate of 0.32±0.09 µSv/hr indicates a low level of radiation exposure. However, this level of radiation is slightly higher than typical background radiation, which is usually around 0.1 to 0.2µSv/hr, but it is still within a range considered safe for long-term exposure
The Role of Algae in Bioenergy: Global Case Studies on Sustainable Biofuel Production and Future Prospects
Full text linkThe growing demand for renewable energy sources has placed significant emphasis on bioenergy as a sustainable alternative to fossil fuels. Among the various bioenergy feedstocks, algae emerged as a promising candidate due to its high lipid content, rapid growth rates, and ability to thrive in diverse environments. This project aims to explore the role of algae in bioenergy production, with a particular focus on its potential as a biofuel feedstock. The research problem addresses the challenges of scaling algae-based biofuels for commercial use, including issues related to cultivation, harvesting, processing, and cost-effectiveness. To tackle these challenges, a combination of case studies, and a comprehensive review of global algae biofuel projects was conducted. Key methodologies included an analysis case study evaluations of algae biofuel production from regions with varying environmental conditions, including the United States, China, Brazil, and India. The findings highlight that while algae hold great potential for biofuel production, there remain significant barriers, including high production costs and technological limitations. However, case studies from these countries demonstrate promising advancements in algae-based biofuel research. In India, for example, local initiatives are leveraging the country’s vast coastline and agricultural byproducts to scale algae cultivation, with a focus on low-cost, high-efficiency production methods suited to its environmental and economic context. Additionally, integrated production systems and innovative biotechnological solutions have shown improved economic viability in these regions. This research is significant as it offers valuable insights into the feasibility of algae as a sustainable biofuel source, identifying key factors that could facilitate its commercialization and integration into global renewable energy strategies. The conclusions underscore the importance of continued investment in research, infrastructure, and policy support to fully realize algae\u27s potential as a cornerstone of the future bioenergy landscape
Review of Cable Fault Locating Methods and Usage of VLF for Real Cases of High Resistance Fault Location First
Full text linkWe are in the cable age, and cable circuits are the primary carriers for electrical transmission and distribution networks. Cable and its associated accessories faults are always predictable and quite common. Failure of cable circuits can occur at the commissioning stage or while they are in service. Utilities and service providers consider such failures unacceptable as they impact their reliability and business continuity, necessitating the implementation of specific measures to mitigate them. This paper examines the history of cable fault-finding methods and the most effective techniques for locating high-voltage cable and accessory faults. It identifies the most effective methods based on their ease of use, speed, accuracy, and minimal impact on the cable circuit\u27s life cycle. It also talks about how useful it was to use the very low-frequency (VLF) method to convert high-resistance faults into low-resistance. It also talks about the real failure scenarios for high-resistance faults in high-voltage cable circuits at the commissioning stage
Cloud Carbon Footprint Tracker for Sustainable Practices
Full text linkIn today’s digital era, businesses heavily rely on cloud-based solutions to support their operations, leading to substantial carbon emissions due to the extensive use of cloud resources. As organizations increasingly adopt cloud computing, the environmental impact of these infrastructures has become a growing concern. To address this issue, this paper presents a Carbon Footprint Tracker for Cloud Resources, a comprehensive solution that leverages cloud analytics and artificial intelligence (AI) to measure, analyze, and minimize carbon emissions. Our proposed system integrates seamlessly with Azure Cost Management APIs, Azure Monitor, and advanced Machine Learning models to provide organizations with real-time insights into their cloud consumption and its associated carbon footprint. By analyzing usage patterns and optimizing resource allocation, the system offers data-driven recommendations to enhance sustainability. Additionally, it generates detailed sustainability reports, enabling businesses to track their environmental impact and make informed decisions toward greener cloud strategies. Through this innovative approach, enterprises can effectively reduce their carbon footprint, improve operational efficiency, and align with global sustainability goals. By embracing eco-friendly cloud practices, organizations can contribute to a more sustainable future, ensuring responsible and energy-efficient cloud usage
Novel Machine Learning Approach for Defect Detection in DFT Processes
Full text linkRecent advances in semiconductor technology have highlighted significant challenges in effectively testing modern integrated circuits (ICs). As device densities increase and defect mechanisms become more diverse, conventional Design for Testability (DFT) methodologies – while indispensable – must contend with exponential growth in test complexity. This paper reviews the essential DFT practices, including scan-based structures, boundary scan, and built-in self-test (BIST), and examines how these practices address a variety of logical fault models. It further explores machine learning (ML) techniques as valuable tools for enhancing defect detection and diagnosis. By leveraging classification algorithms such as support vector machines and neural networks, ML-driven approaches can reduce test pattern generation time, improve bridging-fault coverage, and streamline board- or wafer-level screening. Collectively, this paper underscores how strategic synergy between DFT and ML can raise fault coverage, improve diagnostic precision, and contain testing costs in the face of ongoing technology scaling
Cybersecurity in Autonomous Vehicles: Safeguarding Connected Transportation Systems
Full text linkThe increasing integration of autonomous vehicles (AVs) has revolutionized the transport sector, with improved safety, efficiency, and convenience. However, as AVs become more interconnected and integrated into advanced transport systems, the interconnectivity-driven cybersecurity threats present a serious challenge. Current security solutions tend to treat individual systems without taking into account the complexity emanating from interconnected networks, real-time data exchange, and advanced AI-based decision-making systems characteristic of autonomous vehicles. This research tries to fill the crucial gap in autonomous vehicle system cybersecurity frameworks, emphasizing the adoption of a holistic, multi-level approach to secure the vehicle and communication networks. The study explores significant vulnerabilities in AVs, such as vulnerability to remote hacking, data integrity issues, and the risks of system crashes that can jeopardize the vehicle occupants and external stakeholders. It evaluates the effectiveness of current cybersecurity and identifies the loopholes in safeguarding the complex infrastructure behind connected transportation systems. The study also identifies the increasing importance of artificial intelligence and machine learning in identifying and preventing cybersecurity threats in real-time, offering a new direction for proactive threat management. Through an interdisciplinary methodology, the paper proposes a framework for securing AVs and networked transportation infrastructure that uses high-level encryption, AI-assisted anomaly detection, and robust incident response plans. By bridging the cybersecurity gap to the specific autonomous system challenges, this study aims to make it possible to build secure, resilient transportation technology that can scale safely in an increasingly interconnected world. The findings aim to educate policymakers, manufacturers, and researchers on the best practices for securing the autonomous transportation system of the future
MSDRAM: Multivalued Sequence Storage of Random Access Memory Using DNA Technology
Full text linkThe rapid growth in data generation demands innovative solutions for efficient storage and retrieval, far beyond the capabilities of traditional silicon-based Random Access Memory (RAM). DNA-based storage systems have emerged as a revolutionary approach, leveraging DNA\u27s intrinsic properties such as high density, stability, and scalability. Unlike binary encoding, ternary RAM leverages the quaternary nature of DNA bases to represent multivalued data, thereby enhancing storage density and computational efficiency. This technology achieves unprecedented storage densities by mapping multivalued data to synthetic DNA sequences while implementing advanced biochemical techniques for storage. This paper introduces MSDRAM (Multivalued Sequence Storage of Random Access Memory), a novel architecture utilizing DNA technology to overcome the limitations of conventional storage systems. This proposed research sets the foundation for hybrid storage architectures, combining the strengths of molecular and silicon-based technologies to meet future computational demands. The proposed architecture of multivalued SDRAM demonstrates that it achieves a storage density of a single petabyte per gram of DNA, detailing its encoding unit, DNA-based storage medium, and access mechanisms that significantly outperform traditional RAM and binary-based DNA RAM in capacity and heat efficiency. This research highlights the potential of DNA technology for scalable, energy-efficient memory systems and addresses the challenges of heat, speed, and environmental sensitivity