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25th International Arab Conference on information technology, ACIT 2024

作者： Alrjoob, Hmmam M. Alazaidah, Raed Batyha, Radwan Khafajeh, Hayel Elsoud, Esraa Abu Saeb Al-Sherideh, Ala'a Elrashidi, Ali Alqaraleh, Muhyeeddin Amer, Waleed Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Applied Science Private University Faculty of Information Technology Department of Computer Science Amman Jordan Zarqa University Faculty of Information Technology Department Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Cyper Security Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of Information Technology Department of Software Engineering Zarqa Jordan Zarqa University Faculty on Information Technology Department of Cyber Security Zarqa Jordan

ISBN: (纸本)9798331540012

Psychological disorders are considered chronic illnesses that affect a wide range of populations. Some studies in the United States indicate that one in every eight individuals is affected by a psychological disorder. Additionally, individuals with a psychological disorder often have an impact on the nature of the lives of those around them. It is beneficial to detect the illness in its early stages to increase the likelihood of recovery. In this research, thirteen classification algorithms are used. Each algorithm has an interesting approach. It can be used to analyze several datasets where three datasets specific to Psychiatric patients were selected. The datasets have been chosen from UCI Machine Learning Repository. The datasets were re-analyzed after only 50% of the characteristics were approved after being evaluated by the several feature selection techniques. The main objective of this research is to find the best classifier and feature selection strategy in analyzing datasets of Psychiatric patients. After conducting numerous comparisons, it became evident that, the best classifier for the Autism-Adult-Data dataset are Bagging and Random Forest. As for the best learning strategy, it was found to be Functions and Trees, the best feature selection was obtained using Symmetrical Uncourt Attribute Evaluation. Both IBK and KStar classifier proved to be the best classifier for the Parkinson's dataset, Lazy demonstrated to be the most effective learning strategy, Correlation Attribute Evaluation technique yielded the best feature selection. Logistic emerged as the best classifier for planning-relax dataset, the best learning strategy was Bayes, the best feature selection was found to be Symmetrical Uncert Feature Selection. © 2024 IEEE.

关键词： Contrastive Learning

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MAIDS: malicious agent identification-based data security model for cloud environments

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Cluster Computing 2024年第5期27卷 6167-6184页

作者： Gupta, Kishu Saxena, Deepika Gupta, Rishabh Singh, Ashutosh Kumar Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan School of Computer Science and Engineering The University of Aizu Aizuwakamatsu965-0006 Japan Department of Computer Science The University of Economics and Human Sciences Warsaw01-043 Poland Department of Computer Applications SRM Institute of Science & ampTechnology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad201204 India Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal462003 India

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent’s behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system’s sec

关键词： Forecasting

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Brain Tumour Detection Using Stacked Deep Learning Approaches

Brain Tumour Detection Using Stacked Deep Learning Approache...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Sheneamer, Abdullah Jazan University Faculty of Computer Science & Information Technology Computer Science Department Jazan45142 Saudi Arabia

ISBN: (纸本)9798350327816

A brain tumour is a mass or uncontrolled growth of abnormal cells in the brain. tumours have different types and characteristics and have different treatments. Some tumours are noncancerous (benign tumours), some tumours are cancerous (malignant), and brain tumours can be of any type or size. Given the seriousness of this disease, it must be detected early in order to increase the chance of recovery for the patient. Early detection of the tumour plays a major role in the treatment of the patient, as early detection of the tumour would increase the chances of treatment for the patient. In this paper, we used a stacked scratch-built and transfer learning convolutional neural network model to detect brain tumour with high accuracy. The model was designed and implemented on two datasets. These datasets contain a set of images for brain tumour detection. For testing, we used our models, VGG16, VGG19, AlexNet, MobileNetV2 and InceptionResNetV2 individually and further improved the accuracy of the results by integrating all these models into one model and ultimately got better results than the previous models. Experimental results are excellent when compared with contemporary brain tumour detectors. Interestingly, we achieve between 99.5% to 100% success in detecting brain tumour based on recall, precision, and F1-Score. © 2023 IEEE.

关键词： component formatting insert style styling

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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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An underwater explorer remotely operated vehicle: Unraveling the secrets of the ocean

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IEEE Potentials 2023年第3期42卷 31-36页

作者： Huang, Xiang-Rui Chen, Liang-Bi National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Magong880011 Taiwan

In recent years, underwater robots have been an essential focus of marine science and technology applications. Whether it is the application of military tasks or general civil affairs, underwater robots have played a significant role. For example, unmanned underwater vehicles (UUVs) can work in the sea for a long time, have high maneuverability, and perform various underwater tasks. There are many subcategories of UUVs, such as autonomous underwater vehicles (AUVs), remotely operated vehicles (ROVs), and autonomous and remotely operated vehicles (ARVs) (Kong et al., 2020). © 1988-2012 IEEE.

关键词： Remotely operated vehicles

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Image steganalysis using deep learning: a systematic review and open research challenges

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7761-7793页

作者： Farooq, Numrena Selwal, Arvind Department of Computer Science and Information Technology Central University of Jammu Samba181143 India

Image steganography involves the process of concealing sensitive information in the cover image to achieve secret communication. The counterpart of steganography is image steganalysis, which is used to detect any hidden information that is being communicated among different entities. The image steganalysis has gained much attention in the recent past from the information law enforcement as well as with the advancements in communication and information technology, the techniques available for steganography make it more challenging to detect steganographic content. Several steganalysis techniques are available as reported in the literature and each technique relies on the underlying steganographic method used. In this study, we expound an in-depth review of existing state-of-the-art steganalysis approaches and also discuss the evolving intelligent approaches i.e. deep learning (DL) that may be used for image steganalysis. We also present a comparative analysis of various benchmark datasets and evaluation metrics that are commonly used for image steganalysis algorithms. Besides, we also illustrate some openly available tools that may be used for the task of image steganalysis. Our analysis identifies several open research issues in this active field of DL-based image steganalysis and we also present different frameworks that exist in the literature. This study can serve as a reference document to the investigators for future directions in research on deep learning-based steganalysis. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Steganography

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High Utility Periodic Frequent Pattern Mining in Multiple Sequences

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computer Modeling in Engineering & sciences 2023年第10期137卷 733-759页

作者： Chien-Ming Chen Zhenzhou Zhang Jimmy Ming-Tai Wu Kuruva Lakshmanna College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Technology Vellore Institute of TechnologyVellore632014India

Periodic patternmining has become a popular research subject in recent years;this approach involves the discoveryof frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic patternmining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodicpatterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequencesis more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences isimportant. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To addressexisting problems, three new measures are defined: the utility, high support, and high-utility period sequenceratios. Further, a new upper bound, upSeqRa, and two new pruning properties were proposed. MHUPFPS usesa newly defined HUPFPS-list structure to significantly accelerate the reduction of the search space and improvethe overall performance of the algorithm. Furthermore, the proposed algorithmis evaluated using several *** experimental results indicate that the algorithm is accurate and effective in filtering several non-high-utilityperiodic frequent patterns.

关键词： Decision making frequent periodic pattern multi-sequence database sequential rules utility mining

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CVTD: A Robust Car-Mounted Video Text Detector

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computers, Materials & Continua 2024年第2期78卷 1821-1842页

作者： Di Zhou Jianxun Zhang Chao Li Yifan Guo Bowen Li Department of Computer Science and Engineering Chongqing University of TechnologyChongqingChina College of Information and Engineering Jingdezhen Ceramic UniversityJingdezhenChina

Text perception is crucial for understanding the semantics of outdoor scenes,making it a key requirement for building intelligent systems for driver assistance or autonomous *** information in car-mounted videos can assist drivers in making ***,Car-mounted video text images pose challenges such as complex backgrounds,small fonts,and the need for real-time *** proposed a robust Car-mounted Video Text Detector(CVTD).It is a lightweight text detection model based on ResNet18 for feature extraction,capable of detecting text in arbitrary *** model efficiently extracted global text positions through the Coordinate Attention Threshold Activation(CATA)and enhanced the representation capability through stacking two Feature Pyramid Enhancement Fusion Modules(FPEFM),strengthening feature representation,and integrating text local features and global position information,reinforcing the representation capability of the CVTD *** enhanced feature maps,when acted upon by Text Activation Maps(TAM),effectively distinguished text foreground from non-text ***,we collected and annotated a dataset containing 2200 images of Car-mounted Video Text(CVT)under various road conditions for training and evaluating our model’s *** further tested our model on four other challenging public natural scene text detection benchmark datasets,demonstrating its strong generalization ability and real-time detection *** model holds potential for practical applications in real-world scenarios.

关键词： Deep learning text detection Car-mounted video text detector intelligent driving assistance arbitrary shape text detector

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Speaker diarization based on X vector extracted from time-delay neural networks (TDNN) using agglomerative hierarchical clustering in noisy environment

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International Journal of Speech technology 2025年第1期28卷 13-26页

作者： Khadar, K. V. Aljinu Sunil Kumar, R.K. Sameer, V.V. Department of Computer Science University of Calicut Tenhippalam Kerala Malappuram673635 India Department of Information Technology Kannur University Mangattuparamba Kerala Kannur670567 India Department of Computer Science Farook College Kerala Kozhikode673 632 India

This paper introduces a speaker diarization system using speaker embedding parameters, specifically the x-vector. By incorporating auto-correlated MFCC features for x-vector extraction using a pre-trained time delay neural network, the system exhibits enhanced adaptability to noise variations. Speaker clustering is accomplished through agglomerative clustering with PLDA scoring as the distance metric, making the system particularly valuable for potential speaker identification, especially in forensic applications. The system’s noise adaptability is thoroughly evaluated by integrating various types of noise such as Red, Pink, and white noise across a wide range of signal-to-noise ratios (20 dB to − 20 dB). Additionally, the system’s performance is comprehensively assessed by varying speech duration and adjusting the number of speakers, highlighting its robustness and effectiveness in real-world scenarios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Vectors

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Optimizing classifier performance using PCA-FLANN: a fast and reliable approach

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Iran Journal of computer science 2025年第2期8卷 457-470页

作者： Jena, Monalisa Behera, Ranjan Kumar Dehuri, Satchidananda Cho, Sung-Bae Department of Computer Science Yonsei University Seoul 03722 South Korea Department of Computer Science & Engineering Indian Institute of Information Technology Jharkhand Ranchi India Department of Computer Science Fakir Mohan University Odisha Balasore India

This work introduces PCA-FLANN, an innovative hybrid model combining principal component analysis (PCA) with functional link artificial neural network (FLANN) to achieve efficient non-linear dimensionality reduction and robust classification. The approach is motivated by the need to optimize feature selection, recognizing that not all features equally contribute to achieving optimal accuracy. PCA is employed to extract the most relevant features from datasets, retaining essential information while significantly reducing dimensionality, and FLANN processes these reduced features to handle non-linear correlations effectively. To assess its performance, PCA-FLANN is evaluated on six widely-used real-world datasets from the KEEL repository and compared against hybrid models such as PCA-RBFN, PCA-SVM, PCA-RF, PCA-MLP, and standalone FLANN. The results reveal that PCA-FLANN outperforms these models in accuracy, with improvements of 8.5%, 2.94%, 9.09%, 11.1%, and 9.69%, respectively. Additionally, the model demonstrates enhanced computational efficiency and reduced complexity, establishing PCA-FLANN as a powerful and precise solution for classification tasks involving dimensionality reduction. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Classification Feature extraction FLANN Neural network PCA

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