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information technology and engineering (HITE), Horizons of

作者： Hafiza Tayyaba Zafar Bilal Tahir Muhammad Amir Mehmood Al-Khawarizmi Institute of Computer Science University of Engineering and Technology Lahore Pakistan Associated With Faculty of Computer and Information Systems Islamic University of Madinah Madinah Saudi Arabia

ISBN: (数字)9798331516055

ISBN: (纸本)9798331516062

The rapid expansion of Instagram content marked by a large volume of photos and comments demands effective methods for discovering topics. While hashtags facilitate the grouping of posts, categorizing these hashtags into specific topics is challenging task due to the multimodal and noisy nature of content on the platform. In this paper, we introduce a novel approach that leverages multimodal features and Vision-Language Models (VLMs) for the topic classification of hashtags. To achieve this, we develop a dataset InstaHash which includes 464 hashtags and 12,345 posts spanning five topic categories: army, politics, sports, judiciary, and religion. We extract text features using the TF-IDF algorithm and visual features using HSV, SIFT, ResNet50, and VGG16 algorithms. Additionally, we introduce 34 binary features derived from the VLM of LLAVA through prompt engineering. These prompt engineering features examine the six aspects of images such as people, symbols and objects, buildings, events, uniforms, and embedded text. We train four classifiers of Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Convolutional Neural Network (CNN) for topic classification of hashtags. Our analysis reveals that combining text and visual features yields an accuracy of 0.89 for topic classification with the CNN classifier. However, by incorporating additional prompt features into the model, we achieve the highest accuracy of 0.96. Moreover, we notice that prompt features related to aspects of people and symbols are the most important features for the topic classification of hashtags.

关键词： Support vector machines Visualization Accuracy Symbols Feature extraction Classification algorithms Convolutional neural networks Prompt engineering Web sites Multimedia communication

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Bridging the Gap: Secure Northbound Interface in SDN with gRPC

Bridging the Gap: Secure Northbound Interface in SDN with gR...

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Advancements in Computing (ICAC), International Conference on

作者： R. M. K. G. Rathnayake K. A. V. Kalana Uditha Dharmakeerthi Amila Senarathne Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

Software Defined Networking (SDN) promises to introduce flexibility and programmability into networks by offering developers a northbound interface (NBI) to create SDN applications. However, current designs and implementations have drawbacks, including the lack of Proper Standardization, Controller-Lock-In Situation, and Loss of Compatibility with SDN applications. Using Google Remote Procedure Call (gRPC) Application Programming Interface (API), the SDN controller can offer network resources, such as statistics, flow information, or topology data, to registered SDN applications. Furthermore, the gRPC enables controller-agnostic and language-agnostic development to enable proactive and reactive applications and services to interact with controller services. Transport Layer Security (TLS) enables confidentiality with encryption, authenticity with mutual authentication, and integrity with Message Authentication Code (MAC). Finally, Attribute-based Access Control (ABAC) and Logging capability for controller services allow fine-grained access control and accountability enforcement to ensure the security goals of the NBI. This research paper provides a complete architecture to converge different Northbound API streams into one secure, language-agnostic, controller-agnostic API stream with the help of the gRPC framework.

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“Hand Model” – A Static Sinhala Sign Language Translation Using Media-Pipe and SVM Compared with Hybrid Model of KNN, SVM and Random Forest Algorithms

“Hand Model” – A Static Sinhala Sign Language Translation...

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Advancements in Computing (ICAC), International Conference on

作者： G.H.M.S.C Gedaragoda W.R.S Wijesinghe Abeywickrama T.R Shashika Lokuliyana Hansika Mahaadikara Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

The study of sign language recognition has been a thriving area of research for nearly twenty years. SSL translation using computer vision relies on extensive training using large number of images or video sequences. Yet, it poses a challenge for languages with limited resources, like Sinhala Sign Language. Due to the absence of accessible datasets for such investigations and lack of SSL recognition systems for real-time scenarios, the authors of this research have developed a completed system that consist of three components which are, static sign recognition, dynamic sign recognition and natural language processing to output a meaningful sentence. This paper describes the models and the results which are developed for static sign recognition. The final model for the Sinhala Static recognition has developed using media-pipe and SVM algorithm. And to compare the results and performance authors have used CNN, SVM, KNN and Random-forest algorithms to compare the results and performance. In this research, authors have made a valuable contribution by introducing a new dataset and developing a system for translating static SSL that gives 99.67% of accuracy and best performance when compared to the other two models and algorithms described in this paper.

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Navigation Design to Improve Interaction Technique in Multi-Browser Virtual Environment

Navigation Design to Improve Interaction Technique in Multi-...

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International Conference on Cognitive Infocommunications (CogInfoCom)

作者： Mochammad Hannats Hanafi Ichsan Cecilia Sik-Lanyi Tibor Guzsvinecz Department of Electrical Engineering and Information Systems Faculty of Information Technology University of Pannonia Veszprem Hungary Department of Informatics Faculty of Computer Science Brawijaya University Malang Indonesia Hungarian Research Network Budapest Hungary Department of Information Technology and its Applications Faculty of Information Technology University of Pannonia Zalaegerszeg Hungary

ISBN: (数字)9798350378245

ISBN: (纸本)9798350378252

Efficient, precise, and intuitive navigation in a Virtual Environment (VE) or in desktop Virtual Reality (VR) is critical to improving user experience. This research introduces three navigation designs for physical embodiment inside VE using a computer mouse for multi-browser VE in desktop VR based on navigation possibilities. Navigation is intended for the First-Person Shooter (FPS) perspective: Intuitive Navigation (IntuNav), Edge Shift Navigation (EdgeNav), and Twist Shift Navigation (TwistNav). IntuNav uses mouse x and y coordinate movements to move and rotate the camera. EdgeNav relies on the cursor positioning at the edge of the screen for camera rotation and movement. TwistNav uses a middle click and right mouse click for camera movement and rotation. The current navigation design has been implemented in Unity with C#. This research proposed three model designs based on feasible navigation possibilities, which shows that each navigation model has unique advantages that can be adapted to user needs and preferences. Based on the test results using the System Usability Scale, IntuNav and EdgeNav obtained good results and can be used for further development.

关键词： Solid modeling Adaptation models Navigation Virtual environments Cameras User experience C# languages Usability

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Predictive HR Analytics: Using Machine Learning to Forecast Workforce Trends and Needs 7

Predictive HR Analytics: Using Machine Learning to Forecast ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Angulakshmi, M. Madhumithaa, N. Dokku, Srinivasa Rao Pachar, Sunita Sneha, K. Sahaya Lenin, D. Vellore Institute of Technology School of Computer Science Engineering and Information systems Vellore India Faculty of Management Srmist Katangulatthur Tamilnadu Chennai India Pvp Siddhartha Institute of Technology Department of Business Administration Kanuru Andhra Pradesh Vijayawada520007 India Gla University Ibm Department India Saveetha School of Engineering Simats Department of Ece TN Chennai602105 India

ISBN: (纸本)9798350350067

The present research paper aims to analyze the use of predictive HR analytics integrated with machine learning algorithms for the anticipation of workforce demand and requirements. Thus, based on Kaggle's IBM HR Analytics and Employee Satisfaction Index datasets, the study uses Decision Trees and Random Forest approaches to develop the model and predict essential HR parameters, including attrition rates, job satisfaction, and performance levels. This paper illustrates that with the use of machine learning, it is possible to extract valuable information from the data concerning employees and apply them to the field of Human Resource Management to improve the efficiency of decision-making. The results identify the future opportunities of predictive analytics by illustrating its application for HR functions to reduce the efforts toward proactive management, enhance the methods for recruitment, and enhance the approaches to retention of employees. Thus, the findings of this study enrich the body of knowledge regarding the application of machine learning in improving the field of human resource management, specifically in identifying the best approaches for connecting HR efforts with the strategic objectives of organizations. © 2024 IEEE.

关键词： Adversarial machine learning

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Bee Colony-Reptile Search Optimization Technique for Blood Cell Cancer Detection

Bee Colony-Reptile Search Optimization Technique for Blood C...

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International Conference on computer and Automation engineering, ICCAE

作者： Aziza I. Hussein Mohammed Ali Saleh Rabab Hamed M. Aly Energy and Technology Research Center College of Engineering Effat University Jeddah Saudi Arabia Computer and Systems Engineering Department Faculty of Engineering Helwan University Egypt Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt The Higher Institute for Management and Information Technology Minya Egypt

ISBN: (数字)9798331533816

ISBN: (纸本)9798331533823

The biosystem is a crucial system grounded in classification and detection, utilizing Artificial Intelligence (AI) approaches or metaheuristic techniques. Currently, cancer of the blood cells is among the deadliest cancers in the world. Acute lymphoblastic leukemia (ALL) is a cancer of blood cells that causes excessive proliferation of lymphocytes. It is extremely time-consuming and expensive to conduct diagnostic calculations. The number of platelets in a patient's blood is computed by a platelet count. A lacking number of platelets can indicate cancer, infection, or other health problems. A patient with too many platelets is at risk for blood strokes. A single drop of blood includes tens of thousands of platelets. The main goal of this paper is how to detect the features of blood cells and classify with predicting cancer type based on platelets analysis by using Bee Colony followed by Reptile Search Optimization (BCRSO) technique. According to the results, BCRSO algorithm performed better in terms of classification efficacy and accuracy rate than other algorithms. Based on simulation results, the proposed method is more effective than previously published research for classification optimization.

关键词： Accuracy Blood platelets Simulation Metaheuristics Cells (biology) Prediction algorithms Cancer detection Classification algorithms Artificial intelligence Cancer

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Exploring advanced feature selection techniques: an application to dialectal Arabic data

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International Journal of information technology (Singapore) 2024年第7期16卷 4637-4649页

作者： Abdellah, Ait Elouli Ouahi, Hassan Cherrat, El Mehdi Bekkar, Abdellatif Department of Computer Science Laboratory of Innovation in Mathematics and Intelligent Systems Faculty of Applied Sciences Ibn Zohr University Ait Melloul Morocco Laboratory of Innovation in Mathematics and Intelligent SystemsFaculty of Applied Sciences Ibn ZohrUniversity Ait Melloul Morocco Laboratory of Systems Engineering and Information Technology National School of Applied Sciences Ibn Zohr University Agadir Morocco Department of Computer ScienceLIM LaboratoryFaculty of Sciences and Technics Hassan II University of Casablanca Mohammedia Casablanca Morocco

In the field of automated language processing, distinguishing between Moroccan Arabic (Darija) in multilingual contexts is a major challenge. This study addresses this challenge by exploiting feature selection techniques to improve the accuracy of tongue detection. Using a comprehensive methodology integrating various feature selection methods, including TF-idf, CBOW, Word2Vec for feature extraction, LASSO regression, decision trees for machine learning techniques, and statistical feature selection such as ANOVA, Pearson correlation coefficient, and Mutual information, alongside semantic techniques by developing our semantic encoders adapted to Arabic data, we strive to uncover the most important, relevant language features while attenuating the noise. As a result, we have obtained robust results by applying the famous XGBOOST, with conventional extraction methods and SVD as a dimensional reduction method with a very reasonable execution time. Our experimental results highlight the effectiveness of feature selection techniques to reinforce supervised learning tasks. This research not only advances the field of natural language processing but also highlights the central role of feature selection in deciphering complex linguistic landscapes. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Comparative Analysis Dialect Identification Dimensionality Reduction Moroccan Dialect Arabic NLP Standard Arabic

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Human Faces Detection and Tracking for Crowd Management in Hajj and Umrah

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computers, Materials & Continua 2022年第6期71卷 6275-6291页

作者： Riad Alharbey Ameen Banjar Yahia Said Mohamed Atri Abdulrahman Alshdadi Mohamed Abid Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Electrical Engineering Department College of EngineeringNorthern Border UniversityArarSaudi Arabia Laboratory of Electronics and Microelectronics(LR99ES30) Faculty of Sciences of MonastirUniversity of MonastirTunisia College of Computer Sciences King Khalid UniversityAbhaSaudi Arabia CES Laboratory ENISUniversity of SfaxTunisia

Hajj and Umrah are two main religious duties for *** help faithfuls to perform their religious duties comfortably in overcrowded areas,a crowd management system is a must to control the entering and exiting for each *** the number of people is very high,an intelligent crowd management system can be developed to reduce human effort and accelerate the management *** this work,we propose a crowd management process based on detecting,tracking,and counting human faces using Artificial Intelligence *** detection and counting will be performed to calculate the number of existing visitors and face detection and tracking will be used to identify all the humans for security *** proposed crowd management system is composed form three main parts which are:(1)detecting human faces,(2)assigning each detected face with a numerical identifier,(3)storing the identity of each face in a database for further identification and *** main contribution of this work focuses on the detection and tracking model which is based on an improved object detection *** improved Yolo v4 was used for face detection and *** has been very effective in detecting small objects in highresolution *** novelty contained in thismethod was the integration of the adaptive attention mechanism to improve the performance of the model for the desired *** wise attention mechanism was applied to the output layers while both channel wise and spatial attention was integrated in the building *** main idea from the adaptive attention mechanisms is to make themodel focus more on the target and ignore false positive *** demonstrated the efficiency of the proposed method through expensive experimentation on a publicly available *** wider faces dataset was used for the train and the evaluation of the proposed detection and tracking *** proposed model has achieved good results with 91.2%of mAP and a processing speed

关键词： Crowdmanagement Hajj and Umrah face detection object tracking convolutional neural networks(CNN) adaptive attention mechanisms

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods 8

Detection of Detonators in Baggage Using Deep CNN Architectu...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Lynda, Oulhissane Mostefa, Merah Moldovanu, Simona Moraru, Luminita LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Laboratory of Signals and Systems Mostaganem Algeria Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Modelling & Simulation Laboratory MSlab Department of Computer Science and Information Technology Galati Romania Faculty of Sciences and Environment Modelling & Simulation Laboratory Dunarea de Jos University of Galati MSlab Department of Chemistry Physics & Environment Galati Romania

ISBN: (纸本)9798350309249

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL © 2024 IEEE.

关键词： Feature extraction

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Performance Evaluation of Intrusion Detection System with SVM

Performance Evaluation of Intrusion Detection System with SV...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Ricky Aurelius Nurtanto Diaz I Ketut Gede Darma Putra Made Sudarma I Made Sukarsa Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Bali Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Various machine learning techniques have been proposed to improve the effectiveness of Intrusion Detection systems (IDS), where IDS is one of the important parts of the network that functions to maintain network security. With its various capabilities and reputation, Support Vector Machine (SVM) is often applied in various classification cases. However, it has constraints with accuracy and computing time when it comes to data that has large dimensions. Utilizing metaheuristic techniques is one possibility that researchers can use to solve this problem. To overcome this, in this study, we will evaluate the performance of SVM for the IDS case by utilizing the metaheuristic Grey Wolf Optimization (GWO) algorithm. From the experiments with three different iteration models, the best results of the combination of SVM and GWO were found in a model with 10 GWO iterations and 10 populations with an accuracy of 97.47% using 15 features out of 48 original features of the UKM-IDS20 dataset.

关键词： Support vector machines Performance evaluation Accuracy Metaheuristics Intrusion detection Machine learning Network security Intelligent systems Cybernetics

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