Hasil untuk "Information technology"

Xiao Wang, Mengyu Wang, Xueqian Bai et al.

To advance research in multi-agent reinforcement learning (MARL) for pursuit–evasion scenarios, this paper introduces a novel algorithm called Expert Knowledge and Opponent Modeling Multi-UAV Deep Deterministic Policy Gradient (EO-MADDPG). EO-MADDPG consists of two key components: the integration of expert knowledge and real-time sampled data and the prediction of evader UAV actions. The expert knowledge includes a multi-UAV formation control algorithm and an encirclement strategy, which incorporates consensus algorithms and Apollonius circle guidance. Additionally, the network-training framework is optimized by integrating information about opponent actions under a fixed policy for improved prediction accuracy. The experiments focus on three vs. one and three vs. two scenarios, where pursuer UAVs utilize EO-MADDPG and evader UAVs follow fixed policies with Gaussian perturbations. Experimental results show that EO-MADDPG achieves success rates of 99.9 ± 0.3% and 97.5 ± 1.4% (mean ± std over five seeds) in three vs. one and three vs. two pursuit–evasion simulations, respectively, outperforming the baseline MADDPG (72.7 ± 6.0% and 64.4 ± 34.4%). Ablation studies and cooperative landmark tasks further demonstrate improved training stability and interpretability.

Natalia Sedova, Olga Druzhinina

This paper addresses nonlinear time-varying cascade systems governed by differential equations with finite delay. Several sufficient conditions for asymptotic stability are derived, based on differing assumptions regarding the isolated subsystems and their interconnection. The cascade structure enables the treatment of a broad class of systems while simplifying stability analysis compared to conventional approaches. Moreover, it allows the stabilization problem to be decoupled: under suitable conditions, the asymptotic stability of the overall cascade system follows from the stability properties of its individual subsystems. These properties are typically verified using the direct Lyapunov method. In contrast to existing results, the theorems presented herein apply to an extended class of systems and impose relaxed conditions on the Lyapunov functions employed to establish uniform asymptotic stability. Additionally, new results are provided on semiglobal exponential stability and (non-uniform) asymptotic stability for time-varying cascade systems with delay. Collectively, these contributions broaden the applicability of the direct Lyapunov method to delayed cascade systems.

Azhi Kh. Bapir, Ismail Y. Maolood, Dana A Abdullah et al.

The aim of this study is to assess the impact of information technology (IT) on university graduates in terms of employment development, which will aid in economic issues. This study uses a descriptive research methodology and a quantitative approach to understand variables. The focus of this study is to ascertain how graduates of Kurdistan regional universities might use IT to secure employment and significantly contribute to the nation's economic revival. The sample size was established by the use of judgmental sampling procedure and consisted of 314 people. The researcher prepared the questionnaire to collect data, and then SPSS statistical software, version 22, and Excel 2010 were used to modify, compile, and tabulate the results. The study's outcome showed that information technology is incredibly inventive, has a promising future, and makes life much easier for everyone. It also proved that a deep academic understanding of information technology and its constituent parts helps graduates of Kurdistan Regional University find suitable careers. More importantly, though, anyone looking for work or a means of support will find great benefit from possessing credentials and understanding of IT. The study's final finding was that information technology has actively advanced the country's economy. Not only is IT helping to boost youth employment, but it is also turning into a worthwhile investment for economic growth.

Nikit Phadke

We establish a precise mathematical equivalence between witness-based similarity systems (REWA) and Shannon's information theory. We prove that witness overlap is mutual information, that REWA bit complexity bounds arise from channel capacity limitations, and that ranking-preserving encodings obey rate-distortion constraints. This unification reveals that fifty years of similarity search research -- from Bloom filters to locality-sensitive hashing to neural retrieval -- implicitly developed information theory for relational data. We derive fundamental lower bounds showing that REWA's $O(Δ^{-2} \log N)$ complexity is optimal: no encoding scheme can preserve similarity rankings with fewer bits. The framework establishes that semantic similarity has physical units (bits of mutual information), search is communication (query transmission over a noisy channel), and retrieval systems face fundamental capacity limits analogous to Shannon's channel coding theorem.

Teacher Alaa Yahya Younis Saeed

This research addresses the use of computers in supervisory systems within Iraqi commercial banks, by identifying the extent to which these procedures are applied in light of the use of computers, as the theoretical aspect was followed by the descriptive approach of the same title, as the first requirement addresses: the theoretical framework of the internal control system, The second requirement: Problems and limits of the use of the electronic computer. The third requirement: The impact of the use of the electronic computer on the components of internal control, while the practical and analytical aspect was addressed, which is complementary to the theoretical aspect, through a questionnaire and collecting the opinions of the sample community, and then they were analyzed and results and recommendations were reached.

Junpeng Zheng, Ruhao Zhao, Cong Zhang et al.

Generalized two-dimensional (2D) Airy beam is a kind of self-accelerating beam with variable initial angle between its two wings, manifested itself as an initial angle determined parabolic trajectory during the free-space propagation. However, the independent and flexible manipulation of both the transverse optical field and longitudinal trajectory of the generalized 2D Airy beam has not been achieved yet which limits its application in the various fields. Herein, we report on tailoring of the propagation properties of the generalized 2D Airy beam based on the catastrophe theory, where analytical expression of its propagation trajectory is derived. In order to clarify the relationship between the transverse optical field distribution and the longitudinal trajectory, we analytically put forward a generation vector, facilitating the tailoring of both longitudinal trajectory and transverse distribution of optical field simultaneously. Consequently, we can effectively generate the generalized 2D Airy beam and precisely manipulate the evolution of its peak intensity. Once the initial and terminal points of trajectory are determined in advance, we can flexibly tailor the trajectory of 2D Airy beam, with the help of corresponding generation vector. Meanwhile, when the longitudinal trajectory is fixed, we can flexibly rotate the transverse optical field distribution of the generalized 2D Airy beam and manipulate its initial angle. Experimental verifications of the manipulation capabilities for the longitudinal trajectory, initial angle, and the rotation of transverse optical field are provided to validate our theoretical results.

Guoyu Zhang, Ye Tian, Wenhan Yin et al.

The use of automation technology in agriculture has become particularly important as global agriculture is challenged by labor shortages and efficiency gains. The automated process for harvesting apples, an important agricultural product, relies on efficient and accurate detection and localization technology to ensure the quality and quantity of production. Adverse lighting conditions can significantly reduce the accuracy of fruit detection and localization in automated apple harvesting. Based on deep-learning techniques, this study aims to develop an accurate fruit detection and localization method under adverse light conditions. This paper explores the LE-YOLO model for accurate and robust apple detection and localization. The traditional YOLOv5 network was enhanced by adding an image enhancement module and an attention mechanism. Additionally, the loss function was improved to enhance detection performance. Secondly, the enhanced network was integrated with a binocular camera to achieve precise apple localization even under adverse lighting conditions. This was accomplished by calculating the 3D coordinates of feature points using the binocular localization principle. Finally, detection and localization experiments were conducted on the established dataset of apples under adverse lighting conditions. The experimental results indicate that LE-YOLO achieves higher accuracy in detection and localization compared to other target detection models. This demonstrates that LE-YOLO is more competitive in apple detection and localization under adverse light conditions. Compared to traditional manual and general automated harvesting, our method enables automated work under various adverse light conditions, significantly improving harvesting efficiency, reducing labor costs, and providing a feasible solution for automation in the field of apple harvesting.

Anh Tu Tran, Sou Nobukawa, Sou Nobukawa et al.

IntroductionChaotic resonance is similar to stochastic resonance, which emerges from chaos as an internal dynamical fluctuation. In chaotic resonance, chaos-chaos intermittency (CCI), in which the chaotic orbits shift between the separated attractor regions, synchronizes with a weak input signal. Chaotic resonance exhibits higher sensitivity than stochastic resonance. However, engineering applications are difficult because adjusting the internal system parameters, especially of biological systems, to induce chaotic resonance from the outside environment is challenging. Moreover, several studies reported abnormal neural activity caused by CCI. Recently, our study proposed that the double-Gaussian-filtered reduced region of orbit (RRO) method (abbreviated as DG-RRO), using external feedback signals to generate chaotic resonance, could control CCI with a lower perturbation strength than the conventional RRO method.MethodThis study applied the DG-RRO method to a model which includes excitatory and inhibitory neuron populations in the frontal cortex as typical neural systems with CCI behavior.Results and discussionOur results reveal that DG-RRO can be applied to neural systems with extremely low perturbation but still maintain robust effectiveness compared to conventional RRO, even in noisy environments.

Adytyo Wahyu Nugroho, Norhikmah Norhikmah

This massive development of information technology makes it easier for people's lives in various fields, one of them is social media, social media that people use a lot to get information about news or events that are happening in Indonesia, one of which is social media Twitter which provides a lot of information for the people of Indonesia, one of which is information about Covid-19 which is currently rife in the territory of Indonesia Sentiment analysis is a branch of Natural Language Processing (NLP) which can help determine the sentiments that occur in society. This study uses data in the form of tweets to carry out sentiment analysis obtained on Twitter social media.This research utilizes one of the Supervised Learning algorithms, namely Support Vector Machine. In this study, three (3) kernels are used for the Support Vector Machine, each of which is Linear, Radial basis function and Polynomial, to find which kernel produces the highest accuracy value. From the experiments carried out using data sharing for training as much as 70% and for testing data as much as 30% of the total data of 6000 data, the resulting accuracy value for the Support Vector Machine method on the Linear kernel produces an accuracy value of 89% and for the Radial kernel base function accuracy by 90% and for the Polynomial kernel it produces an accuracy of 88%. So it is concluded for the three (3) kernels for testing the Support Vector Machine method on the Radial basis function kernel to produce the best accuracy value

Satanu Ghosh, Neal R. Brodnik, Carolina Frey et al.

We explore the ability of GPT-4 to perform ad-hoc schema based information extraction from scientific literature. We assess specifically whether it can, with a basic prompting approach, replicate two existing material science datasets, given the manuscripts from which they were originally manually extracted. We employ materials scientists to perform a detailed manual error analysis to assess where the model struggles to faithfully extract the desired information, and draw on their insights to suggest research directions to address this broadly important task.

Venelin Todorov, Slavi Georgiev, Ivan Dimov et al.

Nikolaos Kolokas, Dimosthenis Ioannidis, Dimitrios Tzovaras

This paper proposes a generic algorithm for industries with degrading and/or failing equipment with significant consequences. Based on the specifications and the real-time status of the production line, the algorithm provides decision support to machinery operators and manufacturers about the appropriate lifetime extension strategies to apply, the optimal time-frame for the implementation of each and the relevant machine components. The relevant recommendations of the algorithm are selected by comparing smartly chosen alternatives after simulation-based life cycle evaluation of Key Performance Indicators (KPIs), considering the short-term and long-term impact of decisions on these economic and environmental KPIs. This algorithm requires various inputs, some of which may be calculated by third-party algorithms, so it may be viewed as the ultimate algorithm of an overall Decision Support Framework (DSF). Thus, it is called “DSF Core”. The algorithm was applied successfully to three heterogeneous industrial pilots. The results indicate that compared to the lightest possible corrective strategy application policy, following the optimal preventive strategy application policy proposed by this algorithm can reduce the KPI penalties due to stops (i.e., failures and strategies) and production inefficiency by 30–40%.

Adebimpe Esan, Olayinka Abodunrin, Adedayo Sobowale et al.

Background: The advent of technology allows information to be passed through the Internet at a breakneck speed and enables the involvement of many individuals in the use of different social media platforms. Propagation of fake news through the Internet has become rampant due to digitalisation, and the spread of fake news can cause irreparable damage to the victims. The conventional approach to fake news detection is time-consuming, hence introducing fake news detection systems. Existing fake news detection systems have yielded low accuracy and are unsuitable in Nigeria. Objective: This research aims to design and implement a framework for fake news detection using the Long-Short Term Memory (LSTM) model. Methodology: The dataset for the model was obtained from Nigerian dailies and Kaggle and pre-processed by removing punctuation marks and stop words, stemming, tokenisation and one hot representation. Feature extraction was done on the datasets to remove outliers. The locally acquired dataset from Nigeria was balanced using Synthetic Minority Oversampling Techniques (SMOTE) Long-Short Term Memory (LSTM), a variant of Recurrent Neural Network (RNN)-which solved the problem of losing gained knowledge and information over a long period faced by RNN- was used as the detection model This model was implemented using Python 3.9. The model detected fake news by classifying real and fake news approaches. The dataset was fed into the model, and the model classified them as either fake or real news by processing the dataset through input and hidden layers of varying numbers of neurons. accuracy F1 score and detection time were used as the evaluation metrics. The results were then compared to some selected machine learning models and a hybrid of convolutional neural networks and long short-term memory models (CNN-LSTM). Results: The result shows that the LSTM model on a balanced dataset performed best as the two news classes were accurately classified, giving an average detection accuracy of 92.86%, which took the model 0.42 seconds to detect whether news was real or fake. Also, 87.50% average detection accuracy was obtained from an imbalanced dataset. Compared to other machine learning models, SVM and CNN-LSTM gave 81.25% accuracy for imbalanced datasets and 82.14% and 78.57% for balanced datasets, respectively. Conclusion: The outcome of this research shows that the deep learning approach outperformed some machine learning models for fake news detection in terms of performance accuracy. Unique contribution: This work has contributed knowledge by employing an LSTM model for detecting Nigerian fake news using an indigenous dataset. Key Recommendation: Future research should increase the data size of indigenous datasets for fake news detection to achieve improved accuracy. Keywords: Fake news, SMOTE, accuracy, detection, model, deep learning

Haicui Li, Lei Jing

The goal of this study is to help people to monitor brushing process to maintain their oral quality by providing real-time feedback. In this research, a low-cost toothbrushing monitoring system of brushing regions and brushing force using multimodal sensors and Unity is proposed for toothbrushing quality monitoring. An inertial sensor attached to the handle of a toothbrush and Random Forester Classifier (RFC) model were used to estimate brushing regions; five force sensors clipped on the toothbrush and Random Forest Regression (RFR) model were used to estimate brushing force; a visual interface based on Unity was designed to display detection results in real-time. For brushing region detection, the results show that offline verification accuracy is 97.6%, and average accuracy of online detection method is 74.0%. For brushing force detection, 5 subjects were invited to participate in experiment on both User Dependent (UD) and User Independent (UI). The results show that average Root Mean Squared Error (RMSE) is 22.08g for UD experiment; average RMSE is 37.06g for UI experiment. For this 3D brushing monitoring system, 20 subjects were invited to participate in usability experiment. The results show that 3D brushing monitoring system of this research has good usability, performance, and user satisfaction.

Hyder Akmal S., Sundström Agneta, Chowdhury Ehsanul H.

Purpose: This study explores the knowledge development of network-based market orientation (MO) for the internationalization of disruptive innovation (DI) by small and medium-sized enterprises (SMEs).

Naifeng Wen, Guanqun Liu, Jie Zhang et al.

Abstract Molecular property prediction (MPP) is vital in drug discovery and drug reposition. Deep learning-based MPP models capture molecular property-related features from various molecule representations. In this paper, we propose a molecule sequence embedding and prediction model facing with MPP task. We pre-trained a bi-directional encoder representations from Transformers (BERT) encoder to obtain the semantic representation of compound fingerprints, called Fingerprints-BERT (FP-BERT), in a self-supervised learning manner. Then, the encoded molecular representation by the FP-BERT is input to the convolutional neural network (CNN) to extract higher-level abstract features, and the predicted properties of the molecule are finally obtained through fully connected layer for distinct classification or regression MPP tasks. Comparison with the baselines shows that the proposed model achieves high prediction performance on all of the classification tasks and regression tasks.

Xue Zhou, Keijiro Nakamura, Naohiko Sahara et al.

Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan–Meier survival curves among the three phenotypes had significant difference (pairwise comparison <i>p</i> < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29–3.37, <i>p</i> = 0.003), and 0.26 (95%CI 0.11–0.61, <i>p</i> = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition.

Anoosheh Heidarzadeh, Alex Sprintson

This paper introduces the problem of Private Information Retrieval with Reusable and Single-use Side Information (PIR-RSSI). In this problem, one or more remote servers store identical copies of a set of $K$ messages, and there is a user that initially knows $M$ of these messages, and wants to privately retrieve one other message from the set of $K$ messages. The objective is to design a retrieval scheme in which the user downloads the minimum amount of information from the server(s) while the identity of the message wanted by the user and the identities of an $M_1$-subset of the $M$ messages known by the user (referred to as reusable side information) are protected, but the identities of the remaining $M_2=M-M_1$ messages known by the user (referred to as single-use side information) do not need to be protected. The PIR-RSSI problem reduces to the classical Private Information Retrieval (PIR) problem when ${M_1=M_2=0}$, and reduces to the problem of PIR with Private Side Information or PIR with Side Information when ${M_1\geq 1,M_2=0}$ or ${M_1=0,M_2\geq 1}$, respectively. In this work, we focus on the single-server setting of the PIR-RSSI problem. We characterize the capacity of this setting for the cases of ${M_1=1,M_2\geq 1}$ and ${M_1\geq 1,M_2=1}$, where the capacity is defined as the maximum achievable download rate over all PIR-RSSI schemes. Our results show that for sufficiently small values of $K$, the single-use side information messages can help in reducing the download cost only if they are kept private; and for larger values of $K$, the reusable side information messages cannot help in reducing the download cost.

Mariya Evtimova-Gardair

In the paper is proposed a model of multi-agent security system for searching a medical information in Internet. The advantages when using mobile agent are described, so that to perform searching in Internet. Nowadays, multi-agent systems found their application into distribution of decisions. For modeling the proposed multi-agent medical system is used JADE. Finally, the results when using mobile agent are generated that could reflect performance when working with BIG DATA. The proposed system is having also relatively high precision 96%.

Morgan Currie, Benedetta Catanzariti

Human-made datasets carry with them the prejudices and assumptions of their creators. Can art subvert and expose the process?