Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area eve...
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Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin
Blockchain rewriting is necessary for modifying illegal or messages included in blockchain transactions, while maintaining the consistency of subsequent blocks in the blockchain. However, arbitrary blockchain rewritin...
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The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management *** machine learning–based...
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The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security Information and Event Management systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.
The Internet has been enhanced recently by blockchain and Internet of Things(IoT)*** Internet of Things is a network of various sensor-equipped *** gradually integrates the Internet,sensors,and cloud *** is based on e...
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The Internet has been enhanced recently by blockchain and Internet of Things(IoT)*** Internet of Things is a network of various sensor-equipped *** gradually integrates the Internet,sensors,and cloud *** is based on encryption algorithms,which are shared database technologies on the *** technology has grown significantly because of its features,such as flexibility,support for integration,anonymity,decentralization,and independent *** nodes in the blockchain network are used to verify online ***,this integration creates scalability,interoperability,and security *** the last decade,several advancements in blockchain technology have drawn attention fromresearch communities and *** technology helps IoT networks become more reliable and enhance security and *** also removes single points of failure and lowers the *** recent years,there has been an increasing amount of literature on IoT and blockchain technology *** paper extensively examines the current state of blockchain technologies,focusing specifically on their integration into the Internet of ***,it highlights the benefits,drawbacks,and opportunities of recent studies on security issues based on blockchain solutions into *** survey examined various research papers fromdifferent types of ***,a review of the other IoT applications has been included,focusing on the security requirements and challenges in IoT-based *** research directions are gathered for the effective integration of Blockchain and IoT.
The utilization of Data-Driven Machine Learning (DDML) models in the healthcare sector poses unique challenges due to the crucial nature of clinical decision-making and its impact on patient outcomes. A primary concer...
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作者:
Petkar, Taniya
Faculty of Engineering and Technology Department of Computer Science And Medical Engineering Maharashtra Wardha442001 India
This paper presents a novel line-of-control (LoC) monitoring system that leverages the Internet of Things (IoT) to improve border security. The system creates a strong infrastructure for real-time monitoring throughou...
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In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative ne...
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In this paper, we introduce a new class of polynomials, called probabilistic q-Bernstein polynomials, alongside their generating function. Assuming (Formula presented.) is a random variable satisfying moment condition...
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Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data,...
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To extract important information from the document images, document layout analysis research has been carried out. Previous research analyzes document layouts only for specific document formats. This paper proposes a ...
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