Performance Of Detection System Research Articles

A Comprehensive Survey On Machine Learning-Based Intrusion Detection System for Vehicular Area Network Architectures

Today, the development of communication technologies causes changes in many different areas. One of these areas is VANET (Vehicular Area Network) application area. With the increase in usage areas in the VANET field, ensuring VANET network security has become more critical. Many different systems have been developed to detect attacks on VANET networks. Machine learning-based systems are one of the most widely used methods in developing these intrusion detection systems (IDS). In this article, research on machine learning-based VANET IDS, which has been done recently in the literature, has been carried out. First, VANET architecture and security requirements are presented, then a comprehensive literature summary is given, and comparisons are made on different parameters. As a result, it has been determined that many different machine learning models are used in IDSs and perform high-performance detection. In addition to the machine learning algorithm used in the performance of IDSs, it has been shown that many different parameters play a critical role in the performance. The paper aims to guide new studies in this field with the gains that will increase the performance of intrusion detection systems because of the literature comparison (considering criteria such as machine learning model, simulation tools, dataset, machine learning algorithm, and performance criteria).

Optimizing fraud detection in financial transactions with machine learning and imbalance mitigation

AbstractThe rapid advancement of the Internet and digital payments has transformed the landscape of financial transactions, leading to both technological progress and an alarming rise in cybercrime. This study addresses the critical issue of financial fraud detection in the era of digital payments, focusing on enhancing operational risk frameworks to mitigate the increasing threats. The objective is to improve the predictive performance of fraud detection systems using machine learning techniques. The methodology involves a comprehensive data preprocessing and model creation process, including one‐hot encoding, feature selection, sampling, standardization, and tokenization. Six machine learning models are employed for fraud detection, and their hyperparameters are optimized. Evaluation metrics such as accuracy, precision, recall, and F1‐score are used to assess model performance. Results reveal that XGBoost and Random Forest outperform other models, achieving a balance between false positives and false negatives. The study meets the requirements for fraud detection systems, ensuring accuracy, scalability, adaptability, and explainability. This paper provides valuable insights into the efficacy of machine learning models for financial fraud detection and emphasizes the importance of striking a balance between false positives and false negatives.

Intelligent Video Monitoring and Analysis System for Power Grid Construction Site Safety Using Wireless Power Transfer

Power grid construction significantly enhances power grid management and risk control. Inconsistent operations at construction sites can jeopardize grid stability and crew safety. Traditional power lines are less favored due to mobility limitations, while batteries add burden and impracticality. To address this, a wireless power transfer and video monitoring system is developed using RF technology and Yolo V3 model. This enables continuous monitoring and employee safety analysis. The system's detection performance is optimized using HPSO, surpassing existing methods in accuracy and speed. It ensures real-time monitoring and improves the detection of potential risk sources, crucial for construction site safety.

Self‐Assembled Colloidal Photonic Structures for Directional Radioluminescence of Gd and Ta Oxide Scintillators

AbstractRadiation detection is being revolutionized by integrating photonic elements into scintillators. In this study, a scalable and cost‐effective method is proposed to achieve tuneable emission enhancement across the visible spectrum by colloidal self‐assembly of photonic crystals on scintillator surfaces. This concept is demonstrated for Eu3+/Tb3+‐doped Gd and Ta oxides. Widely available and affordable colloidal nanospheres of SiO2 or polymethyl methacrylate are self‐assembled on these scintillators. The size of the nanospheres is carefully optimized to match the desired emission lines of Eu3+/Tb3+. The result is homogeneous and closely‐packed structures with clear photonic bandgap in the visible range. Under X‐ray excitation, the scintillators covered with the photonic layers exhibit enhanced light extraction in the direction perpendicular to the surface, compared to isotropic emission in the bare scintillator. Such scintillation directionality, when optically matched with a proper detector, will result in higher efficiency of the overall detection system. Moreover, X‐ray imaging demonstrates an enhancement of 25% in system resolution of the scintillator supplemented with the photonic layer compared to unmodified scintillators. The proposed method is scintillator‐ and nanosphere‐agnostic, thus offering a promising versatile approach for directing the scintillation light toward a photodetector and increasing detection system performance, including high‐resolution imaging applications.

Progressive Monitoring of Micro-Dumps Using Remote Sensing: An Applicative Framework for Illegal Waste Management

Illegal dumping is one of the major issues to be addressed by public managers in both developed and developing countries. The adequate tackling and enforcement of such unlawful activities require continuous territory monitoring, the lack of which is the principal cause of the failure of traditional systems based on patrolling, eventually triggered by sparse crowdsourced data. This work proposes the digitalization of the entire illegal waste management monitoring process through an innovative decision support system based on multiscale remote sensing data. These data enable the assessment of the risk level of specific areas, thus determining inspection priorities in line with the available patrolling resources. The proposed methodology provides a tool to assess the impact of diverse monitoring system parameters on overall the performance in light of the expected operating costs and to understand whether remote sensing can help a better management of illegal waste in a specific operational scenario, thus helping in the determination of the best cost–benefit trade-off. Simulation results over a demonstration scenario, based on realistic dumping/restoration dynamics and the typical performance of satellite detection systems, show that the adoption of remote sensing technologies in the monitoring process can increase the restoration capacity by about 10% compared to traditional patrolling.

A multivariate approach for fuzzy prediction interval design and its application for a climatization system forecasting

The existing uncertainties present during the operation of dynamic processes could affect the performance of modeling systems, control strategies, and fault detection systems. Because of that, the study of uncertainty quantification and the design of prediction intervals has recently gained interest in representing the effect of uncertainty on future process behavior. However, these tools have been designed to focus on applying multiple-input single-output (MISO) systems. For this reason, this work aims to design a novel multiple-variable uncertainty region that results from the adaptation of prediction interval methods to a multiple-input multiple-output (MIMO) framework. To achieve this goal, the algorithm integrates the information provided by the prediction intervals identified for each separate output variable and the covariance matrix of the past error vectors to determine the ellipsoid region that will contain the future values of the modeled MIMO system. How this proposal handles the uncertainty effect of these multiple output variables is tested over simulated data from a coupled Hénon map and real data from a heating, ventilation, and air conditioning system. The simulation results achieved in this work show the proposal’s effectiveness in characterizing the uncertainty of multiple variables simultaneously when compared to applying the direct combination of multiple MISO prediction intervals.

TRAFFIC SIGN RECOGNITION IN CHALLENGING WEATHER CONDITIONS USING CONVOLUTIONAL NEURAL NETWORKS

The ability of autonomous driving systems to recognize traffic signs in a variety of environmental conditions is crucial to their reliability. This study uses convolutional neural networks (CNNs) to provide a novel method for improving the accuracy of traffic sign recognition systems under challenging weather situations. The research focuses on developing a CNN-based model that is trained using the augmented German Traffic Sign Recognition Benchmark (GTSRB) dataset, which contains over 50,000 labeled images of road signs across 43 categories. To attempt to overcome the limitations of traditional CNN models under adverse environmental conditions, this work presents adaptive feature extraction layers that are especially made to reduce visibility problems brought on by rain, fog, and snow. By taking a comprehensive approach, the model uses advanced data augmentation methods to simulate different weather scenarios, greatly increasing the diversity of the training dataset. Through an analysis of theoretical and practical aspects, the study demonstrates how CNNs enhance the accuracy and efficiency of road sign detection systems in a different weather condition. Research evaluates the model's effectiveness using metrics such as precision, recall, and F1-score, proving its capability to reduce false positives and accurately detect relevant road sign instances. Additionally, the paper highlights the importance of careful dataset preparation and augmentation, model optimization, and training enhancements to improve the performance of road sign detection systems. The results of this research offer benefits for intelligent transport systems, autonomous driving, and road safety, pointing towards further advancements in precise and dependable road sign recognition technology.

TRAFFIC SIGN RECOGNITION IN CHALLENGING WEATHER CONDITIONS USING CONVOLUTIONAL NEURAL NETWORKS

The ability of autonomous driving systems to recognize traffic signs in a variety of environmental conditions is crucial to their reliability. This study uses convolutional neural networks (CNNs) to provide a novel method for improving the accuracy of traffic sign recognition systems under challenging weather situations. The research focuses on developing a CNN-based model that is trained using the augmented German Traffic Sign Recognition Benchmark (GTSRB) dataset, which contains over 50,000 labeled images of road signs across 43 categories. To attempt to overcome the limitations of traditional CNN models under adverse environmental conditions, this work presents adaptive feature extraction layers that are especially made to reduce visibility problems brought on by rain, fog, and snow. By taking a comprehensive approach, the model uses advanced data augmentation methods to simulate different weather scenarios, greatly increasing the diversity of the training dataset. Through an analysis of theoretical and practical aspects, the study demonstrates how CNNs enhance the accuracy and efficiency of road sign detection systems in a different weather condition. Research evaluates the model's effectiveness using metrics such as precision, recall, and F1-score, proving its capability to reduce false positives and accurately detect relevant road sign instances. Additionally, the paper highlights the importance of careful dataset preparation and augmentation, model optimization, and training enhancements to improve the performance of road sign detection systems. The results of this research offer benefits for intelligent transport systems, autonomous driving, and road safety, pointing towards further advancements in precise and dependable road sign recognition technology.

Enhancing retinal disease diagnosis through AI: Evaluating performance, ethical considerations, and clinical implementation

BackgroundThe research problem addresses the need for accurate and efficient detection of retina diseases using artificial intelligence (AI) technologies. The specific aim is to evaluate the performance, ethical considerations, and clinical implementation of AI-driven retina disease detection systems. MethodsThis study is a systematic review. Data sources assessed included various electronic databases searched up to July 31, 2023. The prespecified criteria for study inclusion were studies involving AI algorithms for retina disease detection, including those focused on diabetic retinopathy, age-related macular degeneration, and glaucoma. Participant eligibility criteria encompassed human subjects of all ages, and the interventions assessed were AI-based diagnostic tools compared to traditional diagnostic methods. Only randomized controlled trials and observational studies set in clinical environments were included, covering a time span from the inception of AI technology. FindingsThe search identified 145 studies, of which 61 met the inclusion criteria and were eligible for analysis. The narrative summary of findings indicated that AI algorithms generally demonstrated high accuracy, sensitivity, and specificity in detecting retinal diseases. Deep learning algorithms showed a sensitivity of 90 % and specificity of 98 % for diabetic retinopathy detection. However, several studies highlighted concerns about algorithmic bias, data privacy, and the need for diverse and representative datasets to ensure generalizability across different populations. InterpretationThe AI-driven retina disease detection systems have significant potential to improve diagnostic accuracy and efficiency in clinical practice. Ethical considerations regarding patient privacy, the risk of algorithmic bias, and the challenges of integrating AI into existing healthcare workflows must be addressed. The study underscores the importance of ongoing validation, ethical scrutiny, and interdisciplinary collaboration to harness the benefits of AI while mitigating its risks, ensuring responsible and equitable implementation in clinical settings.

Efficiency of Federated Learning and Blockchain in Preserving Privacy and Enhancing the Performance of Credit Card Fraud Detection (CCFD) Systems

Increasing global credit card usage has elevated it to a preferred payment method for daily transactions, underscoring its significance in global financial cybersecurity. This paper introduces a credit card fraud detection (CCFD) system that integrates federated learning (FL) with blockchain technology. The experiment employs FL to establish a global learning model on the cloud server, which transmits initial parameters to individual local learning models on fog nodes. With three banks (fog nodes) involved, each bank trains its learning model locally, ensuring data privacy, and subsequently sends back updated parameters to the global learning model. Through the integration of FL and blockchain, our system ensures privacy preservation and data protection. We utilize three machine learning and deep neural network learning algorithms, RF, CNN, and LSTM, alongside deep optimization techniques such as ADAM, SGD, and MSGD. The SMOTE oversampling technique is also employed to balance the dataset before model training. Our proposed framework has demonstrated efficiency and effectiveness in enhancing classification performance and prediction accuracy.

Twist detection Mach-Zehnder interferometer based on optical vernier effect and its phase demodulation system

Fiber interferometric sensor can achieve sensing enhancement of various physical quantities based on the optical vernier effect, but there are currently no proposed torsion detection enhancement schemes. In this paper, we control the number of cladding modes in the sensing single-mode fiber by graded index fiber collimator and form a dual beam interference with the core mode, resulting in a transmission spectrum with regular waveform. The experimental results indicate that the sensing Mach-Zehnder interferometer (MZI) has a torsional sensitivity of −0.014 nm/(rad/m) within a large detection range of 0–60 rad/m. After amplification by optical cursor effect, it reaches −0.361 nm/(rad/m) with an amplification factor of 25.8 times, the detection limit for envelope valley is 0.345 rad/m. The sensor has a relatively small response to other physical quantities, with a strain sensitivity of 20.4 pm/με, a temperature sensitivity of 1.792 nm/℃, and a refractive index sensitivity of 77.885 nm/RIU, exhibiting excellent anti refractive index crosstalk performance. In addition, we discuss the phase demodulation scheme for interferometric sensor based on optical vernier effect sensitivity enhancement for the first time, which can filter out noise caused by dispersion or loss, eliminate the influence of the initial phase of the sensing system, and evaluate the direction of improving the detection performance of the phase demodulation system.

Comprehensive study on the development of an automatic helmet violator detection system (AHVDS) using advanced machine learning techniques

The number of motorcycle accidents has increased rapidly due to different causes. Helmet is a one of the major safety equipment that helps to reduce the fatal accidents even though many of the motorcyclists are not used that. Currently many researchers have focused on traffic violator detection but they are yet to identify the violated person details as it requires high resolution cameras and expensive sensors for number plate extraction. This research article is focused on comprehensive study on the development of an automated helmet violation detection with number plate extraction in real time. This study divides the dataset into rider with helmet, rider without helmet, passenger with helmet and passenger without helmet classes, and four training model was created to detect helmet in rider and passengers. The proposed model was constructed in three stages to attain high accuracy with a vast amount of training dataset. In the first stage, the helmet was detected in the input image using the deep learning object-detection model .In the second stage, if the helmet identification confirmed in the input image then the entire motorcyclist image is segmented based on object segmentation model. In the third stage, number plate training model is deployed into the same object detection and object segmentation model to identify and segment the license plates in the motorcyclist image. This study primarily incorporates the Mask Region based Convolution Neural Networks (Mask R-CNN) for object detection and segmentation Further, it incorporates the three different deep neural networks like Dense block Network (DenseNet-121), Residual Network (ResNet-101) and Inception Residual Network architecture V2layers (Inception Res Net V2) to create accurate helmet violator detections with more extensive information. The experimental results indicates that this comprehensive helmet violator detection study improves the helmet detection accuracy by 6.2 %.In addition, the above three comprehensive proposed models are evaluated under Wilcoxon rank test for better sensitive information. Further, the less object detection loss (2 %) and significant computational time variation (69 %) indicates the performance of the comprehensive helmet violator detection system was good compared to existing study.

A bizarre synthesized cascaded optimized predictor (BizSCOP) model for enhancing security in cloud systems

Due to growing network data dissemination in cloud, the elasticity, pay as you go options, globally accessible facilities, and security of networks have become increasingly important in today's world. Cloud service providers, including AWS, Azure, GCP, and others, facilitate worldwide expansion within minutes by offering decentralized communication network functions, hence providing security to cloud is still remains a challenging task. This paper aims to introduce and evaluate the Biz-SCOP model, a novel intrusion detection system developed for cloud security. The research addresses the pressing need for effective intrusion detection in cloud environments by combining hybrid optimization techniques and advanced deep learning methodologies. The study employs prominent intrusion datasets, including CSE-CIC-IDS 2018, CIC-IDS 2017, and a cloud intrusion dataset, to assess the proposed model's performance. The study's design involves implementing the Biz-SCOP model using Matlab 2019 software on a Windows 10 OS platform, utilizing 8 GB RAM and an Intel core i3 processor. The hybrid optimization approach, termed HyPSM, is employed for feature selection, enhancing the model's efficiency. Additionally, an intelligent deep learning model, C2AE, is introduced to discern friendly and hostile communication, contributing to accurate intrusion detection. Key findings indicate that the Biz-SCOP model outperforms existing intrusion detection systems, achieving notable accuracy (99.8%), precision (99.7%), F1-score (99.8%), and GEO (99.9%). The model excels in identifying various attack types, as demonstrated by robust ROC analysis. Interpretations and conclusions emphasize the significance of hybrid optimization and advanced deep learning techniques in enhancing intrusion detection system performance. The proposed model exhibits lower computational load, reduced false positives, ease of implementation, and improved accuracy, positioning it as a promising solution for cloud security.

Managing and Evaluating Data Integrity in machine Learning-Based Cyber Intrusion Detection

This study investigated the impact of data quality on the performance of intrusion detection systems. Experiments were conducted using intrusion datasets and machine learning models. The pre-trained models were less affected by data duplications and overlaps compared to classic ML models. Removing overlaps and duplicates from training data improved the pre-trained models' performance in most cases, but had adverse effects in datasets with highly similar sequences. The study also proposed a framework for model selection and data quality assurance for building high- quality intrusion detection systems. Additionally, we focus on optimizing nine hyperparameters within a 1D-CNN model, using two well-established evolutionary computation methods—genetic algorithm(GA) and particle swarm optimization(PSO).The performances of these methods are assessed using three majordatasets—UNSW-NB15,CIC- IDS2017, and NSL-KDD. The key performance metrics considered in this study include the accuracy, loss, precision, recall, and F1-score. The results demonstrate considerable improvements in all metrics across all datasets, for both GA- and PSO-optimized models, when compared to those of the original non optimized 1D-CNN model.

A Review of Machine Learning Approaches for Rumour Detection: Techniques, Challenges, and Future Directions (Machine Learning for Rumour Detection)

Rumours and misinformation propagate rapidly across online social networks, posing significant challenges to maintaining the integrity of information dissemination. In recent years, machine learning (ML) techniques have emerged as promising tools for automating the detection and mitigation of rumours. This review paper provides a comprehensive examination of the advancements in rumour detection using ML approaches. The paper begins by outlining the landscape of rumour dissemination in online social networks, highlighting the characteristics and challenges associated with rumour detection. Subsequently, it systematically categorizes and analyzes various ML methods employed for rumour detection, including supervised, unsupervised, and semi-supervised learning approaches. Furthermore, the review delves into the diverse features and representations utilized in ML models for rumour detection, such as textual content, user engagement patterns, network structures, and temporal dynamics. It discusses the strengths and limitations of different feature sets and their impact on the effectiveness of rumour detection systems. Moreover, the paper explores the intricacies of dataset construction and evaluation methodologies for training and testing rumour detection models. It examines commonly used benchmark datasets and evaluation metrics, emphasizing the importance of robust evaluation frameworks for assessing the performance of ML-based rumour detection systems accurately. Additionally, the review identifies key challenges and open research questions in the field of rumour detection using ML, including handling evolving rumour patterns, addressing adversarial attacks, and enhancing the interpretability and explain ability of ML models. It also discusses potential directions for future research aimed at advancing the state-of-the-art in rumour detection and mitigation.

Empowering Smart City IoT Network Intrusion Detection with Advanced Ensemble Learning-based Feature Selection

This study presents an advanced methodology tailored for enhancing the performance of Intrusion Detection Systems (IDS) deployed in Internet of Things (IoT) networks within smart city environments. Through the integration of advanced techniques in data preprocessing, feature selection, and ensemble classification, the proposed approach addresses the unique challenges associated with securing IoT networks in urban settings. Leveraging techniques such as SelectKBest, Recursive Feature Elimination (RFE), and Principal Component Analysis (PCA), combined with the Gradient-Based One Side Sampling (GOSS) technique for model training, the methodology achieves high accuracy, precision, recall, and F1 score across various evaluation scenarios. Evaluation on the UNSW-NB15 dataset demonstrates the effectiveness of the proposed approach, with comparative analysis showcasing its superiority over existing techniques.

An effective ensemble classification framework for intrusion detection system using LSTM autoencoder

In intrusion detection, the curse of dimensionality and the trade-off between maintaining a low false alarm rate and achieving a high detection rate are significant challenges. This research suggests a unique strategy based on dimensionality reduction methods to improve the performance of network intrusion detection systems (NIDS). Compressing high-dimensional network traffic data using a Long Short-Term Memory Autoencoder (LSTMAE) allows the reduced characteristics to be submitted to a classifier to identify anomalies that may indicate an attack. Using standard datasets, including Network Security Laboratory - Knowledge Discovery in Datasets (NSL-KDD), UNSW-NB15, and Canadian Institute for Cyber Security - Intrusion Detection Systems (CICIDS2017), the proposed model is tested with classifiers like Random Forest (RF) and LightGBM (Light Gradient Boosting Machines). It is hoped that by adopting this method, NIDS response times may be improved while costs associated with storing and processing data are minimized. Precision, recall, F-score, accuracy, detection rate (DR), and false alarm rate (FAR) are only a few of the performance measures used to assess the quality of the suggested models. The experimental findings show that the proposed LSTMAE model reduces prediction errors more effectively than classic machine learning techniques such as Random Forest (RF), Gradient Boosting (GB), Support Vector Machines (SVM), Deep Belief Networks (DBN), Deep Neural Networks (DNN), Autoencoder (AE), and Long Short-Term Memory (LSTM). The results also show that the proposed solution outperforms the state-of-the-art methods of detection accuracy and computing complexity using accuracy, precision, recall, F1_Score, detection rate, and FAR.

A calibration methodology for the novel radioluminescence detector systems

Radioluminescence mapping using well-characterized and optimized optical systems is an effective method for localizing contamination with alpha-emitting radionuclides. To apply this novel approach to radiological emergency management, nuclear safeguards, nuclear decommissioning, and nuclear forensics, an established traceability chain is required. This work presents the development and implementation of a novel calibration methodology to provide valuable information about, and confidence in, the performance of radioluminescence detection systems. The proposed calibration methodology is based on two complementary approaches: (a) application of well-characterized activity standards to establish a traceable relationship between radioluminescence intensity and alpha activity, and (b) use of all optical radiation-based devices that, when calibrated against an alpha activity standard, simulate the radioluminescence induced in nitrogen (N2) and nitric oxide (NO) gases by alpha particles in specific spectral regions. A dedicated 210Po alpha activity standard with a narrow peak of less than 32keV FWHM at 5.3MeV has been developed and used to characterize a lens-based radioluminescence detection system in terms of its sensitivity to alpha-induced radioluminescence in different atmospheres (air, N2, N2 ＋ NO mixture) in the UV-A and UV-C (solar blind) spectral regions. The characterized lens system was then used as an intermediary transfer device to cross-calibrate two portable integrating sphere-based radiance standards designed to simulate radioluminescence in the UV-A and UV-C spectral regions. Both radiance standards can be used as transfer standards simulating up to 510MBq when used in the UV-A and up to 8.7GBq when used in the UV-C spectral regions. They substantially simplify routine quality control of radioluminescence detection systems by eliminating the need for open alpha sources, which are always associated with strict radiation safety precautions. Furthermore, since radiance standards can operate in a wide range of intensities, linearity and detection limits of radioluminescence detectors can be readily determined. The design, construction, radiometric characterization, and calibration of dedicated transfer standards, as well as the development of new calibration procedures for radiometric traceability of radioluminescence detection systems, will enable appropriate accident and post-accident radiation measurements that will lead to more effective countermeasures and better protection of people, wildlife, and the environment.

Three layer hybrid learning to improve intrusion detection system performance

In imbalanced network traffic, malicious cyberattacks can be hidden in a large amount of normal traffic, making it difficult for intrusion detection systems (IDS) to detect them. Therefore, anomaly-based IDS with machine learning is the solution. However, a single machine learning cannot accurately detect all types of attacks. Therefore, a hybrid model that combines long short-term memory (LSTM) and random forest (RF) in three layers is proposed. Building the hybrid model starts with Nearmiss-2 class balancing, which reduces normal samples without increasing minority samples. Then, feature selection is performed using chi-square and RF. Next, hyperparameter tuning is performed to obtain the optimal model. In the first and second layers, LSTM and RF are used for binary classification to detect normal data and attack data. While the third layer model uses RF for multiclass classification. The hybrid model verified using the CSE-CIC-IDS2018 dataset, showed better performance compared to the single algorithm. For multiclass classification, the hybrid model achieved 99.76% accuracy, 99.76% precision, 99.76% recall, and 99.75% F1-score.

Performance of Flood Early Detection System (FEDS) and Artificial Neural Network on Predicting Flood in the City

An integrated flood detection system that is easily accessible to the public is one of the efforts to reduce the impact of flooding in flood-prone locations. The performance of flood detectors integrated with the internet helps make it easy Public for access information about possibility happening flood. Information about bulk rain, high water level, the water discharge will Becomes indication possibility happening downstream flooding. Tool prototype detector flood this be equipped with temperature and humidity sensor as addition information for society. Research results show that performance tool detector flood already good because capable give information related to data that can be made indication possibility happening flood. Sensors used have score small mistake and after calibrated got score constant for standardize results testing. Bulk sensor test results rain produces an average error of 3% and after calibration obtained constant of 1.03. High sensor test results water level has an average error of 1.07% and after calibrated obtained score constant of 0.98. Humidity sensor testing have the average error value is 3% and after calibration obtained score constant 1.03