Irrelevant Data Research Articles

Wind Characteristics at Agadez and Tahoua Weather Stations

ABSTRACTThis study investigates the characteristics of wind speed in two synoptic weather stations in Niger. Hence, three hourly wind data including wind speed and direction from 2009 to 2019 were used. Prior to the analysis, the dataset was pre‐processed to remove corrupted and irrelevant data. Statistical analysis was conducted on the dataset to understand the variability of the wind speed and wind direction at the two synoptic stations. The results showed that the highest prevailing wind is recorded in the Agadez region with average wind speeds ranging from [3; 6] m/s and [6; 10] m/s at 10 m altitude. The dominant directions are the East from September to May, then the West from June to August. At the Tahoua station, the prevailing winds have average speeds also belonging to [3; 6] m/s and [6; 10] m/s at 10 m altitude, but the dominant directions are East and East‐North‐East from October to April, then West and South‐West from May to September. These findings are useful for the effective planning of a wind energy project in Niger as they provide useful insight regarding the wind characteristics in two windy regions in Niger.

Interrelated dynamic biased feature selection and classification model using enhanced gorilla troops optimizer for intrusion detection

An Intrusion Detection System (IDS) is a valuable tool for network security since it can identify attacks, intrusions, and other types of illegal access. Excessive and irrelevant data slows down the classification process and eventually weakens the system's capacity to make informed decisions when IDS is monitoring a huge volume of network traffic. Innovative approaches are utilized to create large amounts of data and a lot of network traffic in order to test its effectiveness. A vital step in machine learning is feature selection. By determining which features are most essential for describing the dataset and its initial attributes, feature selection seeks to improve intrusion prediction performance while simultaneously delving deeper into the stored data. Making a feature selection is similar to fixing an optimization problem without a clear definition when users don't know where to begin. Finding the fewest characteristics required to describe the dataset, the original features, and to conduct classification is the primary purpose of feature selection, which also aims to improve prediction performance and acquire a deeper understanding of the stored data. As a result, researchers have been focusing on feature-selection issues recently, especially in light of the massive growth in available databases. Metaheuristic algorithms using a learning model have been the subject of studies to optimize feature selection difficulties. This research uses Enhanced Gorilla Troops Optimizer (EGTO), for enhancing the feature selection process and then performing classification. This research presents a Interrelated Dynamic Biased Feature Selection Model using Enhanced Gorilla Troops Optimizer (IDBFS-EGTO) for generation of feature vector set for intrusion detection. Despite its apparent success in handling a wide range of practical problems, it risks getting mired in local optima and premature convergence when faced with more difficult optimization challenges that is overcome with EGTO. The EGTO approach, which uses a collection of operators to strike a more steady equilibrium between exploitation and exploration. The proposed model generates relevant feature subset for machine learning model for accurate detection and classification of intrusions in the network. The proposed model achieved 98.4 % accuracy in intrusion detection and 98.6 % accuracy in EGTO optimization classification. The proposed model is improved by 3.8 % in feature weight allocation accuracy and 1.2 % in detection accuracy levels. The proposed model is compared with the traditional models and the results represent that the proposed model performance is high.

Using generative adversarial network to improve the accuracy of detecting AI-generated tweets.

This paper provides a novel approach using state-of-the-art generative Artificial Intelligence (AI) models to enhance the accuracy of machine learning methods in detecting AI-generated texts; the underlying generative capabilities are used along with ensemble-based learning methods for the exact characterization of created text attributes. Four basic steps are involved in the proposed methodology. The first step of the text process is the preprocessing stage itself consisting of several steps for the purification of irrelevant data. These stages include noise removal, text tokenization, removal of stop-words, word normalization, and handling uncommon words. In the next step, feature engineering and text representations are done whereby every preprocessed text is represented by a square matrix. This matrix encapsulates data about word correlations, cooccurrence, and word weights. The third step is Generative Adversarial Network (GAN)-based feature extraction, using a GAN model to extract efficient features in classifying the texts based on their creator type. After that, it turns the discriminator part into a strong feature extraction model. The fourth step is weighted Random Forest (RF)-based detection, with the features extracted by the discriminator of GAN serving as input to the RF-based detection model. This approach has covered the differences between texts generated by a human and that generated by Artificial Intelligence, with a significant improvement of 99.60% average accuracy, representing a 1.5% improvement against comparative methods.

Adaptive Control of Retrieval-Augmented Generation for Large Language Models Through Reflective Tags

While retrieval-augmented generation (RAG) enhances large language models (LLMs), it also introduces challenges that can impact accuracy and performance. In practice, RAG can obscure the intrinsic strengths of LLMs. Firstly, LLMs may become too reliant on external retrieval, underutilizing their own knowledge and reasoning, which can diminish responsiveness. Secondly, RAG may introduce irrelevant or low-quality data, adding noise that disrupts generation, especially with complex tasks. This paper proposes an RAG framework that uses reflective tags to manage retrieval, evaluating documents in parallel and applying the chain-of-thought (CoT) technique for step-by-step generation. The model selects the highest quality content for final output. The key contributions are as follows: (1) reducing hallucinations by focusing on high-scoring documents; (2) improving real-time performance through efficient retrieval; and (3) mitigating negative effects by filtering out irrelevant information using parallel generation and reflective tagging. These innovations aim to optimize RAG for more reliable, high-quality results.

Bhineka Tunggal Ika and Hasbi ash-Shiddieqy's Indonesian Moderate Fiqh

Before Hasbi ash-Shiddieqy, Indonesian fiqh was predominantly characterized by a rigid and blind imitation (taqlid) rigidly adopting interpretations from Islamic schools in regions such as the Hijaz, Egypt, and India. These foreign teachings often lacked relevance to Indonesia's cultural context, causing friction between Islamic jurisprudence and local customary law (hukum adat). In response, Hasbi developed an Indonesian fiqh that integrated local traditions, aligning with the national motto Bhineka Tunggal Ika, which UN Secretary-General Antonio Guterres praised as crucial for a harmonious future in Southeast Asia. This approach fosters unity through the respectful recognition of cultural diversity. This study explores how sustaining Bhineka Tunggal Ika can be achieved through religious moderation based on Indonesian fiqh, particularly focusing on Hasbi ash-Shiddieqy. Employing qualitative research, the study utilized a literature review and thematic content analysis through four steps: data collection (gathering relevant information), data reduction (filtering out irrelevant data), data display (visually organizing key themes), and inference (synthesising findings to address research questions). This study reveal that Hasbi ash-Shiddieqy's Indonesian fiqh approach strengthens religious moderation by integrating universal Islamic principles with local traditions and promoting peaceful conflict resolution, while being consistent with national regulations-Presidential Regulation 58/2023 and Minister of Religion Regulation 3/2024-that promote unity in diversity and foster social cohesion in Indonesia. Therefore, Hasbi ash-Shiddieqy's Indonesian fiqh modernizes Islamic law by integrating maslahah (public welfare) and urf (cultural practices), moving beyond rigid taqlid, and deserves recognition as a vital source for strengthening religious moderation and sustaining Bhineka Tunggal Ika in Indonesia.

Deep weighted survival neural networks to survival risk prediction

Survival risk prediction models have become important tools for clinicians to improve cancer treatment decisions. In the medical field, using gene expression data to build deep survival neural network models significantly improves accurate survival prognosis. However, it still poses a challenge in building an efficient method to improve the accuracy of cancer-specific survival risk prediction, such as data noise problem. In order to solve the above problem, we propose a diversity reweighted deep survival neural network method with grid optimization (DRGONet) to improve the accuracy of cancer-specific survival risk prediction. Specifically, reweighting can be employed to adjust the weights assigned to each data point in the dataset based on their importance or relevance, thereby mitigating the impact of noisy or irrelevant data and improving model performance. Incorporating diversity into the goal of multiple learning models can help minimize bias and improve learning outcomes. Furthermore, hyperparameters can be optimized with grid optimization. Experimental results have demonstrated that our proposed approach has significant advantages (improved about 5%) in real-world medical scenarios, outperforming state-of-the-art comparison methods by a large margin. Our study highlights the significance of using DRGONet to overcome the limitations of building accurate survival prediction models. By implementing our technique in cancer research, we hope to reduce the suffering experienced by cancer patients and improve the effectiveness of treatment.

Optimizing ESG reporting: Innovating with E-BERT models in nature language processing

Developing a quantitative Environmental, Social, and Governance (ESG) rating tool using technology to reduce manpower requirements and ensure the objectivity and authenticity of the ratings is an important research question in the ESG field. The primary objective of this research is to develop an automated system, encapsulated in the Bidirectional Encoder Representation from Transformers for ESG rating (E-BERT) model, that evaluates ESG information with a high degree of objectivity and consistently delivers reliable rating outcomes. The E-BERT model, an advanced natural language processing (NLP)-based tool, is designed to transform ESG reporting by automating the evaluation process. Utilizing the architecture of Google’s BERT, this model offers precise and consistent ESG ratings, focusing on accurately assessing the sustainability contributions of enterprises. This research is initiated due to the absence of a comprehensive ESG dataset and involves constructing a tailored corpus that supports the E-BERT model. E-BERT meets the demand for objective ESG assessments by filtering out irrelevant data and standardizing criteria across various sectors, thereby streamlining the process and minimizing the need for manual intervention. This model not only automates the rating process but also achieves a notable 93% accuracy rate. By enhancing transparency and reducing biases, E-BERT marks a substantial improvement in ESG reporting, offering a robust tool for stakeholders to reliably assess corporate ESG performance. The success of the E-BERT model confirms its potential as a pivotal resource in supporting informed decision-making for sustainable development.

MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracy

Abstract The rapid development of the global economy and society relies on continuous energy demand, while the severe impact of carbon emissions on the ecological environment has garnered significant international attention. Accurately forecasting carbon emission trends is crucial for developing effective reduction strategies and ensuring sustainable green economic development. In this paper, we propose a Metabolic Neural Network Prediction Model (MNPM) for carbon emissions. This model comprehensively considers the impact of various carbon emission-related factors across different regions in China. By employing one-hot encoding, we address the difficulties traditional classifiers face when handling categorical data, enabling predictions under more realistic conditions. To optimize the nonlinear characteristics of the data and overcome the limitations of grey models, we utilize dynamic iterative time series training to address errors due to data inaccuracies effectively. Experimental results demonstrate that our approach outperforms other methods in filtering out irrelevant data, achieving an average relative residual of 0.055 and an average ratio bias of 0.049, translating to more precise prediction accuracy.

Factors related to the collaboration of cadres in stunting prevention.

In the implementation of stunting prevention, the lack of cooperation between stunting cadres, no direct communication media and no one who facilitates the cooperation of stunting cadres to carry out data collection tasks as indicators of stunting achievement and for stunting prevention. This can result in repeated data collection, unequal data, and irrelevant data accuracy. Stunting cadres, who are community health workers selected and trained to monitor and prevent stunting at the village level, play a crucial role in these efforts. If stunting cadres collaborate, the performance of both will be more effective and efficient. This study aims to find out the factors related to the collaboration of stunting cadres in stunting prevention efforts. A quantitative research method with a cross-sectional study design was employed. The research sample consisted of 63 stunting cadres. Data analysis was conducted using univariate and bivariate analysis. The results showed that variables such as trust (p-value 0.032), communication (p-value 0.021), agreement (p-value 0.002) and added public value (p-value 0.001) were significantly related to the collaboration of stunting cadres. The conclusion of this study is that stunting cadre collaboration is important in stunting prevention efforts and has an impact on optimizing stunting prevention programs.

Pengaruh Metode Tasmi’ dalam Meningkatkan Minat Menghafal Qur’an bagi Siswa di MTs Mualimin UNIVA Medan

This study aims to explore the influence of the Tasmi’ method on increasing students' interest in memorizing the Qur'an. Utilizing a descriptive qualitative approach, the research collects data through interviews, observations, and documentation. Interviews were conducted with Qur'an teachers and students participating in the memorization program to gain in-depth insights into the implementation of the Tasmi’ method. Observations were made during the classroom learning process to see the direct application of the Tasmi’ method and the students' responses to it. Additionally, documentation includes records of students' memorization achievements and relevant learning reports. Data were analyzed through three stages: presentation, reduction, and verification. In the presentation stage, the data were organized systematically, while in the reduction stage, irrelevant data were filtered out, and significant data were retained. In the verification stage, the researcher drew conclusions based on the obtained results. The findings indicate that the Tasmi’ method significantly enhances students' interest in memorizing the Qur'an, with students feeling more motivated and engaged in the memorization process due to this approach. It is hoped that these findings will contribute to the development of more effective Qur'an teaching methods.

Enhancing IoT intrusion detection through machine learning with AN-SFS: a novel approach to high performing adaptive feature selection

The vast volume of redundant and irrelevant network traffic data poses significant hurdles for intrusion detection. Effective feature selection is crucial for eliminating irrelevant information. Presently, most filtering and embedded methods rely on fixed thresholds or ratios, necessitating prior knowledge. Conversely, wrapper methods are computationally intensive, and individual feature selection methods may introduce biases in evaluation. To address these challenges, this study introduces Adaptive Neighborhood based Feature Selection (AN-SFS), a dynamic feature selection approach that adapts to local statistical properties of the data. Unlike traditional methods, AN-SFS adjusts its threshold based on the characteristics of the current feature subset and incorporates statistical measures of neighboring features, capturing subtle relationships and dependencies. This adaptability enables AN-SFS to achieve robust and effective feature selection outcomes. Using NSL-KDD and UNSW-NB15 datasets, our model demonstrates superiority over conventional ML classifiers in detection rate, precision, and recall, achieving outstanding accuracy rates of 99.3% on NSL-KDD and 97.5% on UNSW-NB15, significantly outperforming contemporary methods. To further demonstrate the effectiveness of our feature selection approach, we conducted a series of comparisons with other statistical methods, including ANOVA, Pearson correlation, and the chi-square test. In each comparison, our approach consistently outperformed these alternatives, underscoring its superior efficacy.

An optimized multi-layer ensemble model for airborne networks intrusion detection

In view of the characteristics of airborne networks, such as the traffic data of 1553B data bus and public networks, numerous redundant and irrelevant data, and fewer but more types of attack behaviors, a highly representative balanced data subset is generated by combining K-means with synthetic minority oversampling technique (SMOTE). A feature selection (FS) method of fast correlation-based filter combined with information gain (IG-FCBF) is proposed to filter the key characteristics with 90 % cumulative importance. Aiming at the problem that it is not easy for a single classifier to accurately classify various types of attacks, as well as the difficulty in obtaining the best prediction results by adjusting default and manual parameters, a multi-layer ensemble model based on Bayesian optimization tree-structured Parzen estimator (BO-TPE) is proposed for airborne networks intrusion detection, combining the advantages of supervised learning, stacking ensemble method and hyperparameter optimization (HPO). The experimental results show the superiority and effectiveness of the model, as well as its universality in Integrated Avionics Systems (IAS) and onboard public networks, which provides a new approach for airborne networks intrusion identification and protection.

ResiDEM: Analytical Tool for Isomorphous Difference Electron Density Maps Utilizing Dynamic Residue Identification via Density Clustering.

Time-resolved serial femtosecond crystallography (TR-SFX) of biological molecules captures the time-evolved dynamics of the residual motions across crystal structures, enabling the visualization of structural changes in response to chemical and physical stimuli to elucidate the relationship between the structure and function of the system under study. However, interpretations of residual motions can be complex to deconvolute because of various factors such as the system's size, temporal and spatial complexity, and allosteric behavior away from active sites. Relying solely on electron density map visualization can also pose a challenge in differentiating between useful and irrelevant data. In order to accurately identify residues and determine their respective contributions to the reaction dynamics, new tools are needed. We developed a new tool, ResiDEM, which employs a clustering-based approach to group difference electron densities and associate them with proximal residues. It can identify and rank residues with significant motions. Network representation can be used to delineate the interrelations between the residues in motion. With these features, ResiDEM helps to interpret residual motions in TR-SFX data, identify key residues, and elucidate their roles in dynamic processes.

Node and relevant data selection in distributed predictive analytics: A query-centric approach

Distributed Predictive Analytics (DPA) refers to constructing predictive models based on data distributed across nodes. DPA reduces the need for data centralization, thus, alleviating concerns about data privacy, decreasing the load on central servers, and minimizing communication overhead. However, data collected by nodes are inherently different; each node can have different distributions, volumes, access patterns, and features space. This heterogeneity hinders the development of accurate models in a distributed fashion. Many state-of-the-art methods adopt random node selection as a straightforward approach. Such method is particularly ineffective when dealing with data and access pattern heterogeneity, as it increases the likelihood of selecting nodes with low-quality or irrelevant data for DPA. Consequently, it is only after training models over randomly selected nodes that the most suitable ones can be identified based on the predictive performance. This results in more time and resource consumption, and increased network load. In this work, holistic knowledge of nodes’ data characteristics and access patterns is crucial. Such knowledge enables the successful selection of a subset of suitable nodes for each DPA task (query) before model training. Our method engages the most suitable nodes by predicting their relevant distributed data and learning predictive models per query. We introduce a novel DPA query-centric mechanism for node and relevant data selection. We contribute with (i) predictive selection mechanisms based on the availability and relevance of data per DPA query and (ii) various distributed machine learning mechanisms that engage the most suitable nodes for model training. We evaluate the efficiency of our mechanism and provide a comparative assessment with other methods found in the literature. Our experiments showcase that our mechanism significantly outperforms other approaches being applicable in DPA.

PDSMV3-DCRNN: A novel ensemble deep learning framework for enhancing phishing detection and URL extraction

Phishing is a cyber-attack that exploits victims' technical ignorance or naivety and commonly involves a Uniform Resources Locator (URL). As a result, it is beneficial to examine URLs before accessing them to spot a phishing assault. Several algorithms based on machine learning have been presented to detect phishing attempts. However, these approaches often suffer from lower performance outcomes, such as lower accuracy, longer response times, and higher false positive rates. Furthermore, many existing methods rely heavily on predefined feature sets, which may limit their adaptability and robustness. In contrast, our proposed method leverages a more dynamic feature selection process, which includes the Conditional Wasserstein Generative Adversarial Network (CWGAN) for addressing data imbalance and the Binary Grey Goose Optimization Algorithm (BGGOA) for optimal feature selection. This dynamic approach enhances the model's ability to adapt to varying data characteristics, improving detection performance. The proposed solution is divided into two stages: pre-deployment and deployment. During the pre-deployment stage, the dataset is preprocessed, including data transformation, handling irrelevant and redundant data, and ensuring data balancing. Minority samples are increased using CWGAN to avoid class imbalance. Features are then selected using BGGOA, resulting in a feature-reduced dataset used for training and testing ensemble deep learning classifiers, specifically the Novel Pyramid Depth-wise Separable-MobileNetV3 (PyDS-MV3) and Deformable Convolutional Residual Neural Network (DCRNN), termed PDSMV3-DCRNN. During the deployment phase, the Boosted ConvNeXt approach extracts URL features fed into the trained classifier to predict "phishing" or "benign". According to experimental findings, the proposed solution outperforms all other tested approaches, displaying a faster training time of 0.11 s and achieving an optimal accuracy of 99.21%.

Channel adaptive CVFCN using a new transfer method for PolSAR terrain classification

ABSTRACT This paper mainly addresses the lack of labelled data and insufficient data utilization in PolSAR image classification. We propose a channel adaptive Complex-Valued Fully Convolutional Networks based on Transfer learning (TF-CVFCN). To solve the problem of less labelled data, we borrowed from the transfer learning method, which can use a large amount of easily obtained relevant or even irrelevant data for pre-training and then use a small amount of source data for network training fine-tuning. Since PolSAR data can obtain different data information by using different decomposition methods, the current research trend is to process PolSAR data in the complex-valued containing more information. In our work, to cater to the proposed network model and make the network design more liberal, a new transfer learning method is proposed, which uses a new method to realize partial layer parameter transfer, and achieves the purpose of optimizing the traditional Transfer learning method. On this basis, we optimize the network structure to improve the classification accuracy of PolSAR data, and evaluate its performance on three datasets. Furthermore, we lighten the TF-CVFCN and optimize the relevant parameters using Competitive Multi-Objective Particle Swarm Optimizer, emphasizing the importance of this parameter optimization in large networks.

Occlusion-guided multi-modal fusion for vehicle-infrastructure cooperative 3D object detection

In autonomous driving, leveraging sensor data (e.g. camera, LiDAR data) from both the vehicle and the infrastructure significantly improves perception capabilities. However, this integration traditionally results in increased demands on communication bandwidth. To address these challenges, we introduce Fusion2comm, an occlusion-guided feature fusion approach designed to optimize vehicle-infrastructure cooperative 3D object detection. Our innovative strategy employs an intelligent fusion of camera and LiDAR data to enhance the expressiveness of features. Subsequently, it leverages a segmentation model to extract foreground features and utilizes an occlusion-based selection of communication content, effectively easing bandwidth constraints. We propose a multimodal foreground feature fusion architecture that selectively processes and transmits critical information, substantially reducing irrelevant background data transfer. An innovative occlusion confidence-aware communication technique dynamically adjusts communication regions based on occlusion levels, ensuring efficient data exchange. Fusion2comm sets a new benchmark in the DAIR-V2X dataset, achieving an average precision of 71.25% with minimal bandwidth usage of 221.04 bytes. Our comprehensive experimental evaluations confirm that Fusion2comm substantially advances detection precision while simultaneously improving communication efficiency.

Adaptive-propagating heterophilous graph convolutional network

Graph convolutional networks have significant advantages in dealing with graph-structured data, but most existing methods usually potentially assume that nodes belonging to the same class in a graph tend to form edges, yet inter-class edges exist in many real-world graph-structured data. Due to the propagation mechanism of graph convolutional networks, it is challenging to prevent the interference aggregation from nodes of different classes, which may result in the incorporation of noise and irrelevant data in the outcome, ultimately decreasing the performance of the model. In this paper, we propose a new framework to address this issue on heterophilous graph-structured data. The proposed method comprises two main components. On one hand, the homophily of the graph-structured data is modeled so that the method can adaptively adjust the information propagation process according to the homophily of the edges, and mitigate the influence of inter-class information. On the other hand, the implicit node interaction is captured through the learned feature space, which is then fused with the original interaction to aggregate sufficient intra-class knowledge. Extensive experiments on real-world datasets demonstrate the superiority of the proposed method against current state-of-the-art approaches. 22Code to reproduce our experiments is available at https://github.com/rebridger/APHGCN.

A 4096 channel event-based multielectrode array with asynchronous outputs compatible with neuromorphic processors

Bio-signal sensing is pivotal in medical bioelectronics. Traditional methods focus on high sampling rates, leading to large amounts of irrelevant data and high energy consumption. We introduce a self-clocked microelectrode array (MEA) that digitizes bio-signals at the pixel level by encoding changes as asynchronous digital address-events only when they exceed a threshold, significantly reducing off-chip data transmission. This novel MEA comprises a 64 × 64 electrode array, an asynchronous 2D-arbiter, and an Address-Event Representation (AER) communication block. Each pixel operates autonomously, monitoring voltage fluctuations from cellular activity and producing digital pulses for significant changes. Positive and negative signal changes are encoded as “up” and “down” events and are routed off-chip via the asynchronous arbiter. We present results from chip characterization and experimental measurements using electrogenic cells. Additionally, we interface the MEA to a mixed-signal neuromorphic processor, demonstrating a prototype for end-to-end event-based bio-signal sensing and processing.

Deep Learning Method for Real-Time Fire Detection System for Urban Fire Monitoring and Control

During urban fire incidents, real-time videos and images are vital for emergency responders and decision-makers, facilitating efficient decision-making and resource allocation in smart city fire monitoring systems. However, real-time videos and images require simple and embeddable models in small computer systems with highly accurate fire detection ratios. YOLOv5s has a relatively small model size and fast processing time with limited accuracy. The aim of this study is to propose a method that employs a YOLOv5s network with a squeeze-and-excitation module for image filtering and classification to meet the urgent need for rapid and accurate real-time screening of irrelevant data. In this study, over 3000 internet images were used for crawling and annotating to construct a dataset. Furthermore, the YOLOv5, YOLOv5x and YOLOv5s models were developed to train and test the dataset. Comparative analysis revealed that the proposed YOLOv5s model achieved 98.2% accuracy, 92.5% recall, and 95.4% average accuracy, with a remarkable processing speed of 0.009 s per image and 0.19 s for a 35 frames-per-second video. This surpasses the performance of other models, demonstrating the efficacy of the proposed YOLOv5s for real-time screening and classification in smart city fire monitoring systems.