RF Method Research Articles

Prediction of treatment response and outcome of transarterial chemoembolization in patients with hepatocellular carcinoma using artificial intelligence: A systematic review of efficacy.

To perform a systematic literature review of the efficacy of different AI models to predict HCC treatment response to transarterial chemoembolization (TACE), including overall survival (OS) and time to progression (TTP). This systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines until May 2, 2024. The systematic review included 23 studies with 4,486 HCC patients. The AI algorithm receiver operator characteristic (ROC) area under the curve (AUC) for predicting HCC response to TACE based on mRECIST criteria ranged from 0.55 to 0.97. Radiomics-models outperformed non-radiomics models (AUCs: 0.79, 95%CI: 0.75-0.82 vs. 0.73, 0.61-0.77, respectively). The best ML methods used for the prediction of TACE response for HCC patients were CNN, GB, SVM, and RF with AUCs of 0.88 (0.79-0.97), 0.82 (0.71-0.89), 0.8 (0.60-0.87) and 0.8 (0.55-0.96), respectively. Of all predictive feature models, those combining clinic-radiologic features (ALBI grade, BCLC stage, AFP level, tumor diameter, distribution, and peritumoral arterial enhancement) had higher AUCs compared with models based on clinical characteristics alone (0.79, 0.73-0.89; p=0.04 for CT+clinical, 0.81, 0.75-0.88; p=0.017 for MRI+clinical versus 0.6, 0.55-0.75 in clinical characteristics alone). Integrating clinic-radiologic features enhances AI models' predictive performance for HCC patient response to TACE, with CNN, GB, SVM, and RF methods outperforming others. Key predictive clinic-radiologic features include ALBI grade, BCLC stage, AFP level, tumor diameter, distribution, and peritumoral arterial enhancement. Multi-institutional studies are needed to improve AI model accuracy, address heterogeneity, and resolve validation issues.

AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules.

Background/Objectives: Inflammation serves as a vital response to diverse harmful stimuli like infections, toxins, or tissue injuries, aiding in the elimination of pathogens and tissue repair. However, persistent inflammation can lead to chronic diseases. Peptide therapeutics have gained attention for their specificity in targeting cells, yet their development remains costly and time-consuming. Therefore, small molecules, with their stability, low immunogenicity, and oral bioavailability, have become a focal point for predicting anti-inflammatory small molecules (AISMs). Methods: In this study, we introduce a computational method called AISMPred, designed to classify AISMs and non-AISMs. To develop this approach, we constructed a dataset comprising 1750 AISMs and non-AISMs, each annotated with IC50 values sourced from the PubChem BioAssay database. We computed two distinct types of molecular descriptors using PaDEL and Mordred tools. Subsequently, these descriptors were concatenated to form a hybrid feature set. The SVC-L1 regularization method was implemented for the optimum feature selection to develop robust Machine learning (ML) models. Five different conventional ML classifiers were employed, such as RF, ET, KNN, LR, and Ensemble methods. Results: A total of 15 ML models were developed using 2D, FP, and Hybrid feature sets, with the ET model with hybrid features achieving the highest accuracy of 92% and an AUC of 0.97 on the independent test dataset. Conclusions: This study provides an effective method for screening AISMs, potentially impacting drug discovery and design.

ANALYSIS OF CUSTOMER CHURN PREDICTION USING LOGISTIC REGRESSION, -NEAREST NEIGHBORS, DECISION TREE AND RANDOM FOREST ALGORITHMS

Customer churn predictions (CCPs) and their comprehensive analysis have become prevalent in the global telecom industry over the last five years, driven by advancements in machine learning (ML) technologies. In addition, AI (artificial intelligence) and ML-based predictive methods are currently employed for CCP applications to enhance customer retention. This predictive CCP methodology streamlines customer management processes and ensures sustainable profit growth. The machine learning models focus on identifying features derived from data that is rich in various types of information. This study analyzes CCP for a specific telecom company’s customer dataset using ML methods such as logistic regression (L.R.), -nearest neighbor (-NN), decision tree (D.T.), and random forest (R.F.). The UCI Iranian telecom churn dataset was utilized, and the influence of potential factors leading to customer churn was also considered. Results show that the tuned RF method yielded the best outcomes, with churn tendency analysis achieving a higher AUC score at 0.9042 with the accuracy of 0.9562. The most important feature of the dataset affecting the customer churn was identified as complains whereas the least important feature happened to be tariff plan.

Interannual variations in grassland carbon fluxes and attribution of influencing factors in Qilian Mountains, China

Clarifying the driving factors of grassland carbon sequestration is essential for understanding its role in the regional carbon balance. However, there is a lack of studies on the upscaling of carbon flux in the Qilian Mountains (QLMs) and the driving factors of its interannual variation (IAV). Based on long-term eddy covariance observations in the QLMs, this study estimated the net ecosystem CO2 exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (ER) of the QLMs grassland using four machine learning methods (random forest regression (RF), extremely randomized tree regression (ETR), support vector regression (SVR), and extreme gradient boosting (XGBoost)) to obtain the optimal estimation model. Subsequently, the spatiotemporal variations of GPP, ER, and NEE in the QLMs grasslands were conducted in a comprehensive analysis. The factors influencing the IAV of carbon flux, the contribution of monthly NEE to NEE IAV, and the contribution of different grassland types of NEE to NEE IAV were explored. Our findings revealed that the accuracy and resolution of the grassland carbon flux estimated by the RF method in this study are higher than those of global products. The grassland exhibited a weak carbon sink from 2000 to 2022, with an average NEE of −26.46 ± 6.80 g Cm−2 yr−1, and it acted as a carbon sink from May to September. The spatial distribution pattern of carbon sequestration was “low in the northwest and high in the southeast”. LAI was the key driving factors of IAV for GPP and ER, while NEE IAV was primarily influenced by precipitation and temperature. Climate and vegetation factors primarily regulated NEE IAV by affecting the GPP and ER of plants, and NEE IAV was primarily driven by GPP. Furthermore, NEE in alpine meadows and alpine steppes dominated the NEE IAV of the entire grassland, and summer NEE contributed the most to the NEE IAV. The results will help us to better understand the carbon cycling mechanism in grassland ecosystems and provide new data support and a theoretical foundation for regional carbon cycling research.

Identification of PANoptosis-Related Genes in Community-Acquired Pneumonia Diagnosis.

This study aimed to identify and characterize novel PANoptosis biomarkers for community-acquired pneumonia (CAP) diagnosis. Transcriptomic data from training set GSE196399 and validation sets GSE94916 and GSE202947 were utilized. A PANoptosis gene set was identified by intersecting DEGs linked to CAP, WGCNA hub genes, and PANoptosis-related genes. GO and KEGG analyses were conducted for enrichment analysis. PANoptosis scores were calculated via ssGSEA. Feature genes were identified using SVM-RFE, LASSO regression, and RF methods. Diagnostic performance was assessed via ROC analysis. Immune cell infiltration was evaluated using CIBERSORT. A PPI network was constructed, and a nomogram was developed for CAP prediction. Drug-gene interactions were investigated. qRT-PCR was conducted to confirm feature gene alterations in clinical samples. We identified 7555 DEGs associated with CAP from the GSE196399 dataset. Through WGCNA, a PANoptosis gene set of 39 genes was found, showing significant enrichment in pathways related to apoptosis and inflammation. CAP patients exhibited significantly reduced PANoptosis scores compared to healthy controls, with a marked upregulation in the majority of the PANoptosis gene set in high-score individuals. Four feature genes (ZNF304, AKT3, MAPK8, and ARHGAP10) were identified as potential biomarkers, exhibiting high diagnostic accuracy with AUCs generally above 0.8. These genes also showed significant correlations with M0 macrophages and neutrophils. Drug-gene interaction analysis revealed potential therapeutic agents targeting MAPK8 and AKT3. Validation in clinical samples confirmed gene expression alterations in CAP patients. The identified PANoptosis feature genes demonstrate high diagnostic accuracy for CAP, serving as potential biomarkers and therapeutic targets for CAP.

Wetland classification based on depth-adaptive convolutional neural networks using leaf-off SAR imagery

The recent development of deep learning (DL) techniques has created opportunities for classifying wetlands from remote sensing data (mainly optical data). However, the methods for accurately and efficiently classifying large-scale wetlands using DL and radar data that can be more effective than optical data still needs evaluation. In this study, we developed an end-to-end depth-adaptive convolutional neural network (CNN) for mapping wetlands using leaf-off time-series Sentinel-1 Synthetic Aperture Radar (SAR) imagery along with ancillary data. We examined the inclusion of multi-land cover proximity information and a CNN-based self-supervised SAR denoising procedure for enhancing wetland classification accuracy. The depth-adaptive CNN based on U-Net architecture was designed to classify wetland classes (emergent wetland, scrub-shrub wetland, forested wetland, and open water) in Delaware, U.S. while achieving optimization between model complexity (network depths) and accuracy. Results show that our proposed DL method (OA = 0.93, MIoU = 0.60) not only produced a higher classification accuracy than the traditional RF method (OA = 0.89, MIoU = 0.18) but also had a significantly reduced computational cost compared to established state-of-the-art CNNs (e.g., DeepLabV3+ and DANet) without loss of accuracy. The inclusion of multi-land cover proximity information (especially distances to forest and water) and the CNN-based self-supervised SAR denoising procedure can both enhance wetland classification accuracy, especially for forested wetland using traditional RF methods. These results demonstrated the novelty and efficiency of our proposed DL method for classifying wetlands by combing denoised SAR imagery and ancillary information, which provides insights on integration of DL approach and radar data for supporting operational wetland mapping at large spatial scales.

Drug–target interaction prediction by integrating heterogeneous information with mutual attention network

BackgroundIdentification of drug–target interactions is an indispensable part of drug discovery. While conventional shallow machine learning and recent deep learning methods based on chemogenomic properties of drugs and target proteins have pushed this prediction performance improvement to a new level, these methods are still difficult to adapt to novel structures. Alternatively, large-scale biological and pharmacological data provide new ways to accelerate drug–target interaction prediction.MethodsHere, we propose DrugMAN, a deep learning model for predicting drug–target interaction by integrating multiplex heterogeneous functional networks with a mutual attention network (MAN). DrugMAN uses a graph attention network-based integration algorithm to learn network-specific low-dimensional features for drugs and target proteins by integrating four drug networks and seven gene/protein networks collected by a certain screening conditions, respectively. DrugMAN then captures interaction information between drug and target representations by a mutual attention network to improve drug–target prediction.ResultsDrugMAN achieved the best performance compared with cheminformation-based methods SVM, RF, DeepPurpose and network-based deep learing methods DTINet and NeoDT in four different scenarios, especially in real-world scenarios. Compared with SVM, RF, deepurpose, DTINet, and NeoDT, DrugMAN showed the smallest decrease in AUROC, AUPRC, and F1-Score from warm-start to Both-cold scenarios. This result is attributed to DrugMAN’s learning from heterogeneous data and indicates that DrugMAN has a good generalization ability. Taking together, DrugMAN spotlights heterogeneous information to mine drug–target interactions and can be a powerful tool for drug discovery and drug repurposing.

A novel Bi-LSTM method fusing current and historical data for tunnelling parameters of shield tunnel

Reasonable shield tunnelling parameters play a crucial role in controlling ground stability and enhancing tunnelling efficiency. Predicting shield tunnelling parameters before excavation is of paramount importance. A novel deep learning method is introduced, integrating bidirectional long short-term memory (Bi-LSTM) layers, and fully connected (FC) layers to fuse current and historical data for shield tunnelling parameters prediction. Historical data captures the impact of excavated sections on the current predicted ring, while current data considers present conditions. A feature fusion method eliminates dimensional differences between historical and current data. The resulting tensor, encompassing both data types, is fed into the FC layer to generate predictions. The effectiveness of the method is demonstrated by predicting shield cutter head torque for Qingdao Metro Line 4 in China, outperforming traditional Bi-LSTM, MLP and RF methods significantly. Ablation studies further analyze the impact of different component modules and structural parameters on model performance. Overall, this innovative approach offers accurate shield tunnelling parameters prediction, enhancing ground stability and tunnelling efficiency.

Enhanced Blue Band Vegetation Index (The Re-Modified Anthocyanin Reflectance Index (RMARI)) for Accurate Farmland Shelterbelt Extraction

Farmland shelterbelts are aimed at farmland protection and productivity improvement, environmental protection and ecological balance, as well as land use planning and management. Farmland shelterbelts play a vital role in determining the structural integrity and overall effectiveness of farmland, and assessing the dynamic changes within these protective forests accurately and swiftly is essential to maintaining their protective functions as well as for policy formulation and effectiveness evaluation in relevant departments. Traditional methods for extracting farmland shelterbelt information have faced significant challenges due to the large workload required and the inconsistencies in the accuracy of existing methods. For example, the existing vegetation index extraction methods often have significant errors, which remain unresolved. Therefore, developing a more efficient extraction method with greater accuracy is imperative. This study focused on Youyi Farm in Heilongjiang Province, China, utilizing satellite data with spatial resolutions ranging from 0.8 m (GF-7) to 30 m (Landsat). By taking into account the growth cycles of farmland shelterbelts and variations in crop types, the optimal temporal window for extraction is identified based on phenological analysis. The study introduced a new index—the Re-Modified Anthocyanin Reflectance Index (RMARI)—which is an improvement on existing vegetation indexes, such as the NDVI and the improved original ARI. Both the accuracy and extraction results showed significant improvements, and the feasibility of the RMARI was confirmed. The study proposed four extraction schemes for farmland shelterbelts: (1) spectral feature extraction, (2) extraction using vegetation indexes, (3) random forest extraction, and (4) RF combined with characteristic index bands. The extraction process was implemented on the GEE platform, and results from different spatial resolutions were compared. Results showed that (1) the bare soil period in May is the optimal time period for extracting farmland shelterbelts; (2) the RF method combined with characteristic index bands produces the best extraction results, effectively distinguishing shelterbelts from other land features; (3) the RMARI reduces background noise more effectively than the NDVI and ARI, resulting in more comprehensive extraction outcomes; and (4) among the satellite images analyzed—GF-7, Planet, Sentinel-2, and Landsat OLI 8—GF-7 achieves the highest extraction accuracy (with a Kappa coefficient of 0.95 and an OA of 0.97), providing the most detailed textural information. However, comprehensive analysis suggests that Sentinel-2 is more suitable for large-scale farmland shelterbelt information extraction. This study provides new approaches and technical support for periodic dynamic forestry surveys, providing valuable reference points for agricultural ecological research.

Research on Air Quality Prediction and Its Influencing Factors Based on RF Method

Research on Air Quality Prediction and Its Influencing Factors Based on RF Method

Ensemble machine learning algorithm for cost-effective and timely detection of diabetes in Maiduguri, Borno State

Diabetes is a serious medical condition that severely hinders the body's ability to produce or properly regulate insulin, leading to detrimental carbohydrate metabolism and dangerously high blood sugar levels. This ultimately causes inadequate carbohydrate metabolism and heightened blood glucose levels. Alarmingly, from 2000 to 2019, diabetes-related mortality rates rose by 3%. In the year 2019 alone, diabetes was tragically responsible for nearly 2 million deaths. This groundbreaking research introduces the improved weighted average ensemble learning (WAEL) model as an innovative solution for detecting diabetes. The enhanced WAEL model effectively addresses the overfitting challenge by integrating multiple models that have gained unique insights from the data. The proposed WAEL model ingeniously combines five feature spaces through the grey wolf optimisation (GWO) algorithm to uncover the optimal weight combination. GWO plays a vital role in weight optimization, enabling the reduction of weights in models that are particularly sensitive to noise. The results demonstrated that the improved WAEL achieved an astounding level of accuracy, soaring to 98.90%. The LGBM algorithm followed closely, achieving an impressive accuracy of 85.00%. The RF method recorded an accuracy of 81.00%. When it comes to accurately identifying diabetes, the improved WAEL ensemble model significantly outperformed the other five individual models, as evidenced by metrics such as accuracy, precision, recall, and F1-score. Therefore, the proposed model stands as a compelling alternative tool for healthcare professionals in the early detection of diabetes.

Advanced prediction of rice yield gaps under climate uncertainty using machine learning techniques in Eastern India

The current study focuses on applying machine learning approaches to forecast future Kharif rice yield gaps in eastern India while accounting for climate change implications. To achieve the United Nations Sustainable Development Goals (SDGs), food security must be prioritized. Rice yield has been predicted using Cubist, GBM, MARS, RF, SVM, and XGB machine learning methods, considering six factors: elevation, soil moisture, precipitation, temperature, soil temperature, and actual evapotranspiration. Climatic change scenarios were generated using the latest climatic Coupled Model Intercomparison Project Phase 6 (CMIP6 MIROC6) Shared Socioeconomic Pathways (SSP) 2–4.5 and SSP5-8.5 datasets between 1990 and 2030. Finally, machine learning algorithms were used to identify rice yield gaps to achieve sustainable agricultural intensification. The rice yield validation was completed with 1889 field-based farmer observation records. The results suggest that Murshidabad and Purba Bardhaman districts had very high rice yields (5.60–3.45 t/ha) when using the Cubist model compared to another model. The findings also reveal a poor rice-yielding zone (1.44–0.39 t/ha) in the western region (Purulia) and a northwestern region (half of the west of Birbhum). The Cubist and RF models' maximum and minimum R2 values were 0.73 and 0.72, respectively. The R2 values were also negligible for the XGB, GBM, SVM, and MARS, machine learning models. Projections for rice production in 2030 indicate that the northern and north-eastern regions (Murshidabad and Purba Bardhaman) as well as the southeastern areas (Jhargram and Paschim Medinipur) have the highest yields, categorized as extremely very high (5.56–3.49 t/ha) and high (3.49–2.49 t/ha). A significant rice yield gap (50-40 %) was found in the center and south-east areas (Bankura, Jhargram, and Paschim Medinipur), the northern region (Murshidabad and Birbhum), and the western region (Purulia). In 2030, the north-western region (Birbhum), as well as the middle and south-eastern regions (Bankura, Jhargram, and Paschim Medinipur districts), had the highest proportion of high (50%–40 %) and very high (60%–50 %) rice yield gap. Our findings can contribute to a new viewpoint on agricultural planning and management for sustainable growth in the face of changing climate circumstances.

A hybrid intrusion detection approach based on message queuing telemetry transport (MQTT) protocol in industrial internet of things

AbstractThe number of attacks against Industrial Internet of Things (IIoT) devices has increased over the past years, particularly on widely used communication protocols like Message Queuing Telemetry Transfer (MQTT). The fast increase in IIoT applications brings both critical challenges and technical gaps in cybersecurity. On the other hand, traditional cyber‐attack detection approaches scrap to address and support the run‐time responsibilities of IIoT environments. This study presents a hybrid Genetic Algorithm and Random Forest (GA_RF) method for detecting cyber‐attacks in Industrial Control Machines (ICS) that use MQTT protocol in the IIoT environment. This architecture integrates ICS with edge devices and cloud servers, using a GA_RF algorithm to detect anomalies in data collected by sensors. Normal data is processed locally and then sent to the cloud for storage and return, ensuring continuous monitoring and security. Also, the MQTT‐IOT‐IDS2020 dataset as a real test case was applied for prediction of the proposed GA_RF method with compare to some other powerful machine and deep learning models. The experimental results show that the proposed GA_RF method has an optimum accuracy of 99.87%–100% for detecting cyber‐attacks. This hybrid algorithm also achieved 0–0.0015 in Mean Absolute Error (MAE) and 100% in Precision, Recall, and F‐score factors. This result led to the proposed architecture, which connects the ICS to a server while running GA_RF on the IIoT environment. In conclusion, this study indicates the effectiveness of GA_RF and aims to improve security by using the MQTT protocol in IIoT.

Elevating optical networks: Machine learning approach for optimal resource scheduling and performance boost

SummaryThe increasing demand for massaging networks that are stable and quick needs reevaluations of standard optical networking administration strategies. To improve the efficacy of optical networks by integrating machine learning (ML) approach for the best resource scheduling, this research presents an innovative dynamic block widow optimized random forest (DBWO‐RF) strategy. To implement the DBWO‐driven resource allocation method in accordance with the categorization and clustering findings, the RF method is incorporated with the software defined optical to achieve channel quality assessment after successfully clustering employs the RF approach to achieve channel quality assessment after successfully clustering traffic patterns using the fuzzy C‐means (FCM) algorithm. To lessen the likelihood of blocking, the fragmentation‐function‐fit (FFF) algorithm was provided and the findings indicate that this approach possesses a reduced blocking risk. Using multiple approaches to modulation for various channel quality, the suggested resource allocation system leverages the DBWO approach to distribute the necessary resources based on various “traffic flow (TF)” clustering findings. The examination's outcomes demonstrate that, compared to other techniques under various given load levels, the present study has a reduced blocking risk, a sufficient complexity degree and greater effectiveness in the utilization of spectrum resources.

A Machine-Learning-Driven Pathophysiology-Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations.

Environmental chemicals, such as PFAS, exist as mixtures and are frequently encountered at varying concentrations, which can lead to serious health effects, such as cancer. Therefore, understanding the dose-dependent toxicity of chemical mixtures is essential for health risk assessment. However, comprehensive methods to assess toxicity and identify the mechanisms of these harmful mixtures are currently absent. In this study, the dose-dependent toxicity assessments of chemical mixtures are performed in three methodologically distinct phases. In the first phase, we evaluated our machine-learning method (AI-HNN) and pathophysiology method (CPTM) for predicting toxicity. In the second phase, we integrated AI-HNN and CPTM to establish a comprehensive new approach method (NAM) framework called AI-CPTM that is targeted at refining prediction accuracy and providing a comprehensive understanding of toxicity mechanisms. The third phase involved experimental validations of the AI-CPTM predictions. Initially, we developed binary, multiclass classification, and regression models to predict binary, categorical toxicity, and toxic potencies using nearly a thousand experimental mixtures. This empirical dataset was expanded with assumption-based virtual mixtures, compensating for the lack of experimental data and broadening the scope of the dataset. For comparison, we also developed machine-learning models based on RF, Bagging, AdaBoost, SVR, GB, KR, DT, KN, and Consensus methods. The AI-HNN achieved overall accuracies of over 80%, with the AUC exceeding 90%. In the final phase, we demonstrated the superior performance and predictive capability of AI-CPTM, including for PFAS mixtures and their interaction effects, through rigorous literature and statistical validations, along with experimental dose-response zebrafish-embryo toxicity assays. Overall, the AI-CPTM approach significantly improves upon the limitations of standalone AI models, showing extensive enhancements in identifying toxic chemicals and mixtures and their mechanisms. This study is the first to develop a hybrid NAM that integrates AI with a pathophysiology method to comprehensively predict chemical-mixture toxicity, carcinogenicity, and mechanisms.

Comparing clocks by using pulses of light

Microwave atomic clocks can easily be synchronized across long distances using RF methods. Their more precise optical cousins require a subtler approach.

Identifying Fraud Financial Reports Based on Signs of Income Management Using Machine Learning Technology: The Case of Listed Companies in Vietnam

This study aims to use a measure of earnings management to predict companies whose financial statements have problems. This is an identification measure other than common measures to predict financial statement fraud such as measuring by the M-Score or the Z-score model that many previous studies have applied. In the income management measure, the author uses a measure of abnormal cash flow and abnormal expense flow to consider whether the corporate financial statements have problems or not. To do this, the author uses data from listed non-financial enterprises in the period from 2018 to 2022, with machine learning and deep learning algorithms, of which we focus on three main algorithms: ANN, SVM and RF. The results show that identifying problematic financial statements based on abnormal cash flows is quite effective with an accuracy of over 78% for the SVM method, while if using the RF method, the accuracy reaches over 82% but it is required to accept an increased processing time.

Design and Implementation of Machine Learning Algorithms in Automatic Grading of Students' Assignments

This study introduces machine learning methods for automatically grading students' assignments, catering to the changing demands of education in the digital age. By utilizing diverse techniques, the aim is to simplify the grading process, give prompt feedback, and improve overall efficiency in assessment practices. The research delves into the different types of assignments such as written responses and coding tasks, and presents a range of ML algorithms including NB, Decision Trees (DT), RF, SVM, Linear Regression (LR), KNN, and Ensemble Methods (EMs). The implementation involves extracting relevant features from assignments, preprocessing data to handle noise and outliers, and training models on a varied set of examples. The grading system prioritizes interpretability, accuracy, and efficiency while aligning with educational objectives and grading policies. Evaluation is done using suitable metrics like recall, accuracy, precision, F1 score for each algorithm. The study contributes to the progress of automated grading systems by providing valuable insights for educators on the potential impact of machine learning in enhancing assessment processes. Additionally, the designed algorithms demonstrate adaptability for different types of assignments and serve as a foundation for future enhancements in grading precision and efficiency.

UOWC Integration with ROVs: A Fiber-Optic Approach for Enhanced Underwater Exploration

Underwater Optical Wireless Communication (UOWC) revolutionizes underwater exploration, leveraging seawater's unique property—reduced absorption of blue-green light. This offers superior advantages over traditional communication methods, providing higher bandwidth and lower latency. This paper explores the integration of UOWC with Remotely Operated Vehicles (ROVs), enhancing data exchange and command capabilities in challenging underwater environments. Seawater's unique properties empower UOWC to outperform acoustic and RF methods, crucial for real-time applications. The study utilizes advanced techniques like Fiber-Optic Communication, High Order Sliding Mode Control, and Hybrid Blockage Detection for ROV integration and control. While the abstract focuses on theory and design, it sets the stage for empirical validations, emphasizing the need for future research to quantify improvements in real-world underwater scenarios

New Hybrid Distributed Attack Detection System for IoT

IoT is expressed as a network of physical objects with applications and various technologies that provide data connection and sharing with various devices and systems over the Internet. Security vulnerabilities in IoT devices are one of the biggest security issues in connecting devices to the internet and collecting and processing user data. These vulnerabilities can lead to increased attacks on IoT devices and malicious use of user data. In this article, we discuss these security problems that arise in IoT systems in detail in distributed systems technology. Distributed systems are increasingly used in the modern computing world. These systems are a structure where multiple independent computers communicate with each other for a common purpose. Distributed system technologies have become more common with the development of internet and cloud computing systems. However, the use of distributed systems has brought with it important security challenges such as security vulnerabilities, access controls and data integrity issues. Therefore, the security of distributed system technologies has been an important focus of work in this area. In this study, information about distributed system technologies and security for IoT is given. The all attack types were classified using ANN, developed RF and hybrid model. In RF, all feature vectors created from all datasets (bank and two financial datasets) were also analyzed separately and the classification performance was examined. In addition, a new RF algorithm based on weight values using the Gini algorithm has been proposed. With this algorithm, the traditional RF algorithm has been developed and the success rates have been increased. In addition, a hybrid method was created by classifying the datasets obtained by RF with ANN. With the hybrid method ANN and the enhanced RF method, its accuracy in detecting normal behaviors and attack types was calculated and the success of the methods was presented comparatively. In addition, the working times of the methods were determined.