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Abstract:

Introduction: The widespread adoption of Internet of Things (IoT) devices has transformed multiple industries, enhancing operational efficiency and convenience. However, the rapid expansion of IoT ecosystems also brings forth significant security challenges. Traditional security frameworks often fail to adequately protect these systems due to their large scale, diversity, and limited resources. In response, cloud-based security solutions have emerged as a promising alternative, offering centralized management, advanced authentication techniques, and real-time threat monitoring. Problem Statement: IoT environments are vulnerable to various security risks, including unauthorized access, data breaches, and device manipulation. Existing security mechanisms often fall short when it comes to defending against sophisticated cyber-attacks targeting IoT devices and networks. The resource-constrained nature of many IoT devices further limits the implementation of robust local security measures. As a result, there is an urgent need for effective, cloud-based security solutions designed specifically for the unique demands of IoT systems. Objective: This research aims to explore the effectiveness of cloud-based security solutions in mitigating the security challenges faced by IoT environments and devices. The study focuses on evaluating the performance of cloud-based authentication mechanisms, intrusion detection systems, and encryption techniques in strengthening the security and privacy of IoT ecosystems. Methodology: A comprehensive approach is employed, combining a literature review, case studies, and empirical research to assess the current landscape of IoT security in smart environments. Data collection includes unstructured interviews with industry experts and stakeholders, offering insights into current practices and emerging security trends. The research framework incorporates threat modeling, risk assessments, and the development of proactive security strategies. Results: Initial findings indicate that cloud-based security solutions offer several benefits for protecting IoT environments and devices. Centralized management enhances integration and scalability, while advanced authentication methods, such as multi-factor and biometric authentication, improve access control. Real-time threat detection and response capabilities further bolster security by enabling timely interventions to prevent breaches and attacks. Conclusion: Cloud-based security solutions present a highly effective approach to addressing the unique security concerns of IoT environments and devices. By leveraging the scalability, flexibility, and computational power of cloud platforms, organizations can enhance the resilience of their IoT deployments against evolving cyber threats. However, further research is needed to optimize cloud-based security tools to better serve diverse IoT applications and use cases.

Keywords:

Internet of Things (IoT),Security Solutions,Authentication,Intrusion Detection,Data Encryption,Cloud Computing,

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Abstract:

The comprehensive study investigates the application of cutting-edge machine learning algorithms and advanced image processing techniques for the early detection of lumpy skin disease in cattle. The proposed robust analytical framework that evaluates multiple predictive models using comprehensive performance metrics, including F1 scores ranging from 0.87 to 0.97, precision up to 0.984, recall up to 0.963, and accuracy peaking at 97.77%. The novel approach incorporates pixel-level analysis to quantify disease severity through the ratio of affected to healthy tissue, complemented by processing speed delays between 5.54ms and 20.95ms. The research demonstrates significant improvements over traditional diagnostic methods, with particular emphasis on the model's ability to identify high-risk cases requiring immediate intervention. These findings have substantial implications for veterinary medicine, agricultural technology development, and livestock management policies, potentially revolutionizing disease surveillance systems in the agricultural sector.

Keywords:

Artificial Intelligence,Convolutional neural network,Deep Learning,Lumpy skin disease,Lumpy Pixel Ratio,Machine Learning,Precision livestock farming,

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Abstract:

Drying is a critical post-harvest operation for agricultural and herbal products, but remains energy-intensive and performance-limited under humid tropical conditions. This study experimentally investigates the energy performance and drying kinetics of a photovoltaic–thermal (PVT) solar dryer integrated with porous absorber materials to enhance heat and mass transfer. The developed system consists of a 150 W PVT panel, a double-pass solar collector, and porous steel mesh absorbers with porosities of 0.98, 0.97, and 0.96. Experiments were conducted under tropical climatic conditions in Thailand at a low air velocity of 0.07 m/s using kaffir lime leaves as a representative leafy material. The results show that drying occurred entirely in the falling-rate period and was governed by internal moisture diffusion. Porous integration improved thermal uniformity and airflow turbulence, increasing the drying rate by 43.73–56.01% and reducing specific energy consumption (SEC) by 30.44–36.01% compared with the non-porous configuration. Optimal performance was achieved at a porosity of 0.96, yielding the highest Deff (8.59x10-13 m²/s) and the lowest SEC of 21.84 MJ/kg. Thin-layer analysis confirmed that the Page model best described the drying kinetics (R2 = 0.980–0.999). Statistical analysis (ANOVA) verified that porosity is a dominant factor influencing performance (p < 0.01). Overall, the integration of porous materials into a PVT solar dryer significantly enhances drying performance and represents an energy-efficient approach for drying leafy agricultural products in humid tropical regions.

Keywords:

PVT-porous dryer,photovoltaic-thermal,porous material,solar drying,energy efficiency,

References:

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Abstract:

Background: The growing need for secure communication on resource-constrained systems, such as those in the Internet of Things (IoT), has led to a significant increase in demand for lightweight symmetric ciphers. Nevertheless, different techniques and implementations exist, making the selection of the optimal security solution for a particular application challenging. Objective: This study primarily focuses on implementing an optimised Lightweight Advanced Encryption Standard (LAES) algorithm in hardware to address the critical need for energy-efficient security solutions for IoT devices. Methods: This study implements LAES to meet the security requirements for IoT devices. The Kintex 7 and Spartan 7 FPGAs (Field Programmable Gate Arrays) are utilised for implementation, with critical performance metrics such as hardware area utilisation used to evaluate performance. The algorithm eliminates the computationally expensive MixColumns operation from standard AES while maintaining essential security transformations. Performance evaluation focused on hardware resource utilisation (LUTs, FFs, IO) and power consumption across clock frequencies ranging from 1 ns to 20 ns. Results: The results indicate significant advancements in achieving area and power-efficient designs. Our findings show that the reduction in power consumption is by 95.29% and 92.07% as compared to existing models. The area consumption, such as LUTs, FFs, and IO, has also been significantly decreased compared to existing models. Conclusions: The proposed LAES architecture demonstrates that strategic algorithm optimisation can yield substantial improvements in both power efficiency and hardware utilisation without compromising security, making it highly suitable for IoT deployment.

Keywords:

FPGA,Lightweight Cryptography,IoT Security,Power Optimisation,LAES Algorithm,Area,Data Privacy,VIVADO,

References:

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Abstract:

Introduction: The rapid expansion of Internet of Things (IoT) devices, combined with the widespread adoption of cloud computing, has led to an interconnected digital environment. However, the lack of interoperability and standardization between IoT and cloud systems presents significant challenges. This study explores the importance of addressing interoperability issues and establishing standardized practices to facilitate smoother integration between these technologies. Problem Statement: The diverse range of IoT devices and cloud platforms has created a fragmented ecosystem where interoperability challenges impede the seamless exchange of data and functionality. The absence of universally accepted protocols further complicates compatibility, leading to performance inefficiencies, increased development complexity, and potential security risks. Resolving these issues is essential for unlocking the full capabilities of IoT and cloud integration. Objective: The goal of this research is to examine the current state of interoperability and standardization in IoT and cloud integration. The study aims to identify existing challenges, evaluate current standards and protocols, and propose solutions to enhance interoperability and standardization, fostering a more cohesive and efficient integration between these technologies. Methodology: This study uses a multi-method approach, including a thorough literature review, case studies of existing IoT and cloud integration efforts, and unstructured interviews with industry experts. The analysis focuses on identifying recurring interoperability challenges, evaluating the effectiveness of existing standards, and reviewing successful integration strategies. This comprehensive approach provides a detailed understanding of the complexities surrounding IoT and cloud interoperability. Results: The research identifies key challenges affecting the interoperability of IoT and cloud systems. Through detailed analysis of current standards and successful integration cases, the study offers insights into effective strategies for overcoming these barriers. The results provide actionable recommendations for enhancing interoperability and achieving smoother integration across various IoT and cloud environments. Conclusion: This study emphasizes the urgent need for improved interoperability and standardization in IoT and cloud integration. The research findings, along with proposed solutions, offer valuable direction for industry professionals, policymakers, and researchers working towards creating a more interconnected and efficient digital ecosystem. As IoT and cloud technologies continue to advance, establishing strong, standardized frameworks is crucial to realizing the full potential of these transformative technologies.

Keywords:

IoT integration,Interoperability,Standardization,Cloud computing,Digital ecosystem,Connectivity protocols,

References:

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Abstract:

In this research paper, we have proposed a new technique for resolving the Benjamin-Ono and Buckmaster equations that come up in many engineering and science applications. The double Elzaki transform and the Adomian polynomials are coupled in the suggested hybrid approach. Experiments have been carried out to verify the correctness and simplicity of the suggested scheme. To assess the effectiveness of the suggested scheme, the outcomes so obtained are compared with the results obtained by the variational iteration method.

Keywords:

Double Elzaki Transform,Adomian Decomposition method,Benjamin-Ono Equation,Buckmaster Equations,Variational Iteration Method (VIM),Test examples,

References:

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