期刊涵盖广泛的计算机科学和控制系统相关问题，包括：-软件和硬件工程；-管理系统工程；-信息系统和数据库；-嵌入式系统；-物理系统建模和应用；-计算机网络和云计算;-数据可视化；-人机界面；-计算机图形、视觉分析和多媒体系统。

The journal "Information Technology and Control" covers a wide range of issues related to computer science and control systems, including: software and hardware engineering, management systems engineering, information systems and databases, embedded systems, modeling and applications of physical systems, computer networks and cloud computing, data visualization, human-computer interfaces, computer graphics, visual analysis, and multimedia systems.

《Information Technology and Control》期刊涵盖了广泛的计算机科学和控制系统相关问题，包括：软件和硬件工程、管理系统工程、信息系统和数据库、嵌入式系统、物理系统建模与应用、计算机网络与云计算、数据可视化、人机界面、计算机图形、视觉分析和多媒体系统。

Information Technology and Control

Mining of High Utility Itemset (HUI) is an area of high importance in data mining that involves numerous methodologies for addressing it effectively. When the diversity of items and size of an item is quite vast in the given dataset, then the problem search space that needs to be solved by conventional exact approaches to High Utility Itemset Mining (HUIM) also increases in terms of exponential. This factual issue has made the researchers to choose alternate yet efficient approaches based on Evolutionary Computation (EC) to solve the HUIM problem. Particle Swarm Optimization (PSO) is an EC-based approach that has drawn the attention of many researchers to unravel different NP-Hard problems in real-time. Variants of PSO techniques have been established in recent years to increase the efficiency of the HUIs mining process. In PSO, the Minimization of execution time and generation of reasonable decent solutions were greatly influenced by the PSO control parameters namely Acceleration Coefficient and and Inertia Weight. The proposed approach is called Adaptive Particle Swarm Optimization using Reinforcement Learning with Off Policy (APSO-RLOFF), which employs the Reinforcement Learning (RL) concept to achieve the adaptive online calibration of PSO control and, in turn, to increase the performance of PSO. The state-of-the-art RL approach called the Q-Learning algorithm is employed in the APSO-RLOFF approach. In RL, state-action utility values are estimated during each episode using Q-Learning. Extensive tests are carried out on four benchmark datasets to evaluate the performance of the suggested technique. An exact approach called HUP-Miner and three EC-based approaches, namely HUPEUMU-GRAM, HUIM-BPSO, and AGA_RLOFF, are used to relate the performance of the anticipated approach. From the outcome, it is inferred that the performance metrics of APSO-RLOFF, namely no of discovered HUIs and execution time, outstrip the previously considered EC computations.

K. Logeswaran

P. Suresh

S. Anandamurugan

Information Technology And Control

Particle Swarm Optimization Method Combined with off Policy Reinforcement Learning Algorithm for the Discovery of High Utility Itemset

结合离策略强化学习算法的高效用项集发现粒子群优化方法

COVID cases and its variants is noted enormously in the past three years. In many medical cases, lung infections such as viral pneumonia, bacterial pneumonia have been initially interpreted as COVID-19. Hence, the proposed work is concentrating on differentiating these lung infection types. This work focuses on using neutrosophic approach of classifying into True (T), False (F) and Indeterminacy (I) set membership to reduce the fuzziness and retain more significant information for feature extraction of the opacity to differentiate the types of lung infections. Initially, the images are preprocessed by alpha-mean and beta-enhancement operation to reduce the indeterminacy and enhancing the image components as the range of lung opacity levels to determine the types. Then, these neutrosophic set enhanced images are fed to various deep learning models like ResNet-50, VGG-16 and XGBoost for classification. Experiments are conducted on ActualMed COVID-19 Chest X-ray and COVID-19 radiography dataset and a comparative analysis on several domain set of images such as the original image, neutrosophic domain (T, I, F) and enhanced neutrosophic domain (alpha, beta) are trained and tested through transfer learning by tuning the various validation parameters. On experimental analysis, an enhanced neutrosophic image achieves a better accuracy of 97.33% among the other domain sets.

J. Sofia Jennifer

T. Sree Sharmila

A Neutrosophic Set Approach on Chest X-rays for Automatic Lung Infection Detection

一种基于中智集的胸部X光片自动肺部感染检测方法

To monitor electrical indications from the heart and assess its performance, the electrocardiogram (ECG) is the most common and routine diagnostic instrument employed. Cardiac arrhythmias are only one example of the many heart conditions people might have. ECG records are used to diagnose an arrhythmia, an abnormal cardiac beat that can cause a stroke in extreme circumstances. However, due to the extensive data that an ECG contains, it is quite difficult to glean the necessary information through visual analysis. Therefore, it is crucial to develop an effective (automatic) method to analyze the vast amounts of data available from ECG. For decades, researchers have focused on developing methods to automatically and computationally categorize and identify cardiac arrhythmias. However, monitoring for arrhythmias in real-time is challenging. To streamline the detection and classification process, this research presents a hybrid deep learning-based technique. There are two major contributions to this study. To automate the noise reduction and feature extraction, 1D ECG data are first transformed into 2D Scalogram images. Following this, a combined approach called the Residual attention-based 2D-CNN-LSTM-CNN (RACLC) is recommended by merging multiple learning models, specifically the 2D convolutional neural network (CNN) and the Long Short-Term Memory (LSTM) system, based on research findings. The name of this model comes from a combination of the two deep learning. Both the beats themselves, which provide morphological information, and the beats paired with neighboring segments, which provide temporal information, are essential. Our suggested model simultaneously collects time-domain and morphological ECG signal data and combines them. The application of the attention block to the network helps to strengthen the valuable information, acquire the confidential message in the ECG signal, and boost the efficiency of the model when it comes to categorization. To evaluate the efficacy of the proposed RACLC method, we carried out a complete experimental investigation making use of the MIT-BIH arrhythmia database, which is used by a large number of researchers. The results of our experiments show that the automated detection method we propose is effective.

C. Satheesh Pandian

A. M. Kalpana

HybDeepNet: ECG Signal Based Cardiac Arrhythmia Diagnosis Using a Hybrid Deep Learning Model

HybDeepNet：基于ECG信号的混合深度学习模型用于心脏心律失常诊断

Human gender and age identification play a prominent role in forensics, bio-archaeology, and anthropology. Dental images provide prominent indications used for the treatment or diagnosis of disease and forensic investigation. Numerous dental age identification techniques come with specific boundaries, namely minimum reliability and accuracy. Gender identification approaches are not widely researched, whereas the effectiveness and accuracy of classification are not practical and very minimal. Drawbacks in the existing system are considered in the formulation of the proposed approach. Deep learning approaches can effectively rectify issues of drawbacks in other classifiers. The accuracy and performance of a classifier are enhanced with the deep convolutional neural network. The fuzzy C-Means Clustering approach is used for segmentation, and Ant Lion Optimization is used for optimal feature score selection. The selected features are classified using a deep convolutional neural network (DCNN). The performance of the proposed technique is investigated with existing classifiers, and DCNN outperforms other classifiers. The proposed technique achieves 91.7% and 91% accuracy for the identification of gender and age, respectively.

B. Hemalatha

P. Bhuvaneswari

Mahesh Nataraj

G. Shanmugavadivel

Human Dental Age and Gender Assessment from Dental Radiographs Using Deep Convolutional Neural Network

基于深度卷积神经网络的牙科X光片人类牙齿年龄与性别评估

Breast cancer is a major cause of death among women in both developed and underdeveloped countries. Early detection and diagnosis of breast cancer are crucial for patients to receive proper treatment and increase their chances of survival. To improve the automatic detection and diagnosis of breast cancer, a new deep learning model called “Breast Cancer Prognosis Based Transfer Learning (BCP-TL)” has been developed. This model uses transfer learning, which applies the knowledge gained from solving one problem to another relevant problem. The model is based on a pre-trained convolutional neural network (CNN) that extracts features from the mammographic image analysis society (MIAS) dataset. Four different CNN architectures were used in thismodel: AlexNet, Xception, ResNeXt, and Channel Boosted CNN. The performance of the model was evaluated using six metrics, including accuracy, sensitivity, specificity, precision, F1-score, and the area under the ROC curve (AUC). The combination of Xception and Channel Boosted CNN showed excellent performance. By combining essential features from multiple iterations, the Channel Boosted CNN can achieve higher accuracy in breast cancer diagnosis, with an overall accuracy of 98.96%. This highlights the potential of the BCP-TL model in effectively detecting and diagnosing breast cancer.

M. Diwakaran

D. Surendran

Breast Cancer Prognosis Based on Transfer Learning Techniques in Deep Neural Networks

基于深度神经网络迁移学习技术的乳腺癌预后研究

More than 220 enterprises in China's real estate industry have gone bankrupt, causing serious losses. The National Bureau of Statistics of China showed that the country's investment in property development fell by 8.5% year-on-year, while domestic lending dropped by 11.5% and the use of foreign capital fell by 43%. Upon this, the development of supply chain finance can alleviate the pressure on enterprise funds and stabilize the real estate market. However, risk in supply chain finance is the biggest obstacle to the development of supply chain finance and current researches on risk assessment of supply chain finance face problems such as imprecise classification, slow assessment speed, a small number of samples, and data that is easily tampered with. Therefore, this study integrated graph convolutional neural networks into the smart contracts of the contract layer of blockchain. This integration established a novel intelligent perception model for supply chain finance risk. Based on a consortium chain with the government and enterprises as nodes, the model was established, including risk monitoring, assessment, and categorized early warnings. In the risk assessment part, we compared the graph convolutional neural network with multilayer perceptron and support vector machine finding that the accuracy rate of the graphic convolutional neural network is 94%, which is higher than the above models. The intelligent risk-perception model proposed in this paper operates faster than expert judgment assessments used by banks. It also provides accurate risk levels and quantifies the probability of enterprises being classified as high-risk, offering technical support to regulatory authorities in controlling supply chain financial risk.

Qiang Fu

Mingxia Li

Weiqiang Li

A Novel Risk-Perception Model Based on Blockchain for Supply Chain Finance of China Real Estate

基于区块链的中国房地产供应链金融风险感知新模型

The Internet has led to the rapid development of online education, but it has also caused redundancy in educational information. How to choose appropriate courses from a large number of online education resources has become a major problem for current learners. Therefore, the study proposes an English learning system based on efficient and deep Matrix decomposition. The results of the experiments showed that, in practical teaching applications, about 57.5% of students with good grades have improved their grades due to the use of the English learning system proposed by the research institute, with only about 17.5% of their grades decreasing. 67.5% of students with average grades have improved their grades after using the system, with only 10% decreasing. Among the students with poor grades, about 50% of them improved their academic performance through the system, while about 27.5% of them experienced a decrease. Meanwhile, the experiment also tested the efficient deep Matrix decomposition model in the learning system: the minimum absolute average errors of the model on different data sets are about 0.61, 0.69, 0.77 and 0.82, respectively. The minimum Root-mean-square deviation is about 0.91, 0.98, 1.06 and 1.1, which is far lower than other recommended models. The above results show that the system constructed in this paper can recommend courses accordingto students ‘actual learning level, and can effectively improve students’ academic performance in the actual teaching process.

Jinli Yuan

Design and Implementation of an English Learning System Based on Intelligent Recommendation

基于智能推荐的英语学习系统设计与实现

Parkinson’s disease is caused by a disruption in the chemical products that enables the communication between brain cells. The brain’s dopamine cells are responsible for movement control, adaptability, and fluidity. Parkinson’s motor symptoms manifest when 60–80% of these cells are damaged due to insufficient dopamine. Researchers are working to find a way to identify the non-motor symptoms that manifest early detection in the disease to stop the disease’s progression because it is believed that the disease starts many years before the motor symptoms. This research presents Parkinson’s disease diagnosis based on deep learning. Processes for feature selection and classification encompass the suggested diagnosis technique. The proposed model searches for the best subset of characteristics using the Improved Glowworm Swarm Optimization (IGSO) algorithm. Radial Basis Functions Networks (RBFN) classifiers evaluate the chosen features. The suggested model is tested using datasets from Parkinson’s Handwriting samples and Parkinson’s Speech and voice with various sound recordings. With an accuracy of about 95.78%, the suggested algorithm forecasts Parkinson’s disease using the VoicePD dataset more precisely.

M. Sivakumar

K. Devaki

Improved Glowworm Swarm Optimization for Parkinson’s Disease Prediction Based on Radial Basis Functions Networks

基于径向基函数网络的帕金森病预测改进萤火虫群优化算法

The traditional laser SLAM (Simultaneous Localization and Mapping) algorithm uses the global relative poses and local ones to form residual blocks. Its constructed map is not smooth enough and the constraint construction is too simplex under some special scenarios. Thus, this paper proposes an odometer constraint fusion method called FOSLAM (Fusion Odometer SLAM) to construct residual blocks between constrains and solve the nonlinear least squares by Ceres. The effectiveness and accuracy of this method have been verified through comparative experiments. Experimental results showed that without increasing the time and space complexity, by involving the odometer constraint into the SLAM optimization process, the convergence of scan matching scores can be improved and the constructed grid map edges are smoother and the jagged phenomenon can be reduced. Under sophisticated scene, FOSLAM is able to acquire more accurate maps and laser odometer trajectory than Cartographer method. Therefore, it is suitable to be used on indoor robot for cleaning and inspection and can be further deployed on autonomous unmanned vehicles involving spatial visualization and neuro-heuristic guidance.

Haojun Huang

Puxian Yang

Shengqing Cai

Jixiang Li

Yuda Zheng

Tengyue Zou

A Construction Optimization for Laser SLAM Based on Odometer Constraint Fusion

基于里程计约束融合的激光SLAM建图优化

At present, deep learning techniques are increasingly utilized in computer vision and anomaly detection. To address the limitations of inadequate reconstruction capability and subpar performance in reconstruction-based anomaly detection, this study enhances the existing algorithm and introduces an unsupervised anomaly detection of industrial images algorithm based on dual generator reconstruction networks-DGRNet. The network consists of two generators and a discriminator, introducing a widely recognized denoising diffusion probabilistic model (DDPM) as one of the generators, an autoencoder (AE) as the other generator, and a decoder as the discriminator. The model is tested on the MVTec AD dataset, and in the case of no additional training data,the anomaly detection AUC result of DGRNet exceeds the baseline method based on reconstruction by 19.6 percentage points. The experimental results show that DGRNet can improve the detection performance in the anomaly detection algorithm based on unsupervised and reconstructed networks.

Abbreviation	ITC
Journal Impact	1.94
Quartiles(Global)	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q3)

ISSN	1392-124X, 2335-884X
h-index	22

Publication Information	Publisher: Kauno Technologijos Universitetas，Publishing cycle: Semiannual，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2008，Proportion of original research papers: 97.92%，Self Citation Rate:5.00%， Gold OA Rate: 98.85%
Journal Fees	$Details
Average review cycle	网友分享经验：>12周，或约稿
Average recruitment ratio	网友分享经验：容易

Information Technology and Control

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