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8th Conference on information Communication Technology and Society, ICTAS 2024

作者： Khoza, Nokuthula Owolawi, Pius Malele, Vusumuzi Soshanguve Campus Tshwane University of Technology Faculty of Information and Communication Technology Department of Computer Systems Engineering Pretoria South Africa North-West University Unit for Data Science and Computing School of Computer Science and Information Systems Vanderbijlpark Campus Vanderbijlpark South Africa

ISBN: (纸本)9798350314892

Tracking a person with an onboard camera is a very difficult and perhaps technically impossible if one camera is used. In this regard, real-life projects use a series of cameras to achieve the task. The advent of camera installed drone technology has made it possible for researchers and designers to resolve the tracking issues of a single or multiple stagnant devices. This paper presents a method that makes it possible for a drone to recognize hand gestures as control mechanism and follow the person who control it. Furthermore, the method assists in drone object detection using an integrated depth camera that provides video capturing. The OpenCV and Tello were used in this project. A stabilized depth video is for use with current person trackers like the OpenCV tracker. The estimated Tello pose deduced from vision to stabilize the depth image by warping it to a virtual-static camera. This method allows the Tello to obtain not only the tracked person's position and orientation but also their full body pose, which can then be used, for instance, to recognize hand gestures and alter the Tello's behavior. This work has a potential of contributing or enhancing cutting-edge applications such as hands-free photography and videography. © 2024 IEEE.

关键词： Drones

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CNN Channel Attention Intrusion Detection SystemUsing NSL-KDD Dataset

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computers, Materials & Continua 2024年第6期79卷 4319-4347页

作者： Fatma S.Alrayes Mohammed Zakariah Syed Umar Amin Zafar Iqbal Khan Jehad Saad Alqurni Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadh11362Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Educational Technologies College of EducationImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online *** goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection systems(NIDS)that can identify *** NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection ***,maintaining operating efficiency while improving detection accuracy is the primary goal of this ***,typical NIDS examines both risky and typical behavior using a variety of *** the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel *** to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection ***,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this *** efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

关键词： Intrusion detection system(IDS) NSL-KDD dataset deep-learning machine-learning CNN channel Attention network security

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An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ

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computers, Materials & Continua 2023年第3期74卷 5641-5661页

作者： Afia Zafar Muhammad Aamir Nazri Mohd Nawi Ali Arshad Saman Riaz Abdulrahman Alruban Ashit Kumar Dutta Badr Almutairi Sultan Almotairi Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaMalaysia School of Electronics Computing and MathematicsUniversity of DerbyUK Department of Computer Science National University of TechnologyIslamabadPakistan Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Kingdom of Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia Department of Information Systems Faculty of Computer and Information SciencesIslamic University of MadinahMadinah42351Saudi Arabia

In computer vision,convolutional neural networks have a wide range of *** representmost of today’s data,so it’s important to know how to handle these large amounts of data *** neural networks have been shown to solve image processing problems ***,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher *** technique is time consuming and requires a lot of work and domain *** a convolutional neural network architecture is a classic NP-hard optimization *** the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and *** approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random *** address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized *** study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN *** addition,different types and parameter ranges of existing genetic algorithms are *** study was conducted with various state-of-the-art methodologies and *** have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature.

关键词： Non-dominated sorted genetic algorithm convolutional neural network hyper-parameter optimization

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Hybrid TFT-GRU Time Series Models Optimized by PSO: An Advanced Approach for Financial Data Prediction

Hybrid TFT-GRU Time Series Models Optimized by PSO: An Advan...

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2024 International Conference on Intelligent and Innovative Practices in Engineering and Management, IIPEM 2024

作者： Kumar, Sanjay Kharkwal, Harendra Singh Verma, Atul Singh, Amit Kumar Srivastava, Meenakhi Shri Ramswaroop Memorial University Department of Computer Science and Information Systems Lucknow India Amity University Amity Institute of Information Technology India

ISBN: (纸本)9798350390049

Accurate financial time series prediction is a key challenge in modern quantitative finance, directly influencing decision-making and strategy development. In reply to this challenge, researchers recommend an innovative hybrid model that cartels the strengths of the Temporal Fusion Transformer (TFT) and Gated Recurrent Unit (GRU) models for financial data prediction. The TFT is created for interpretable multi-horizon forecasting and can capture both short- and long-term relationships in time series data, while GRU excels in efficiently handling sequential data with lower computational complexity associated to more traditional recurrent models like LSTMs. This hybrid method influences the unique advantages of both TFT and GRU to build a more robust and flexible predictive *** advance the precision and efficiency of the hybrid TFT-GRU model, we employ Particle Swarm Optimization (PSO) for hyperparameter tuning. PSO, a nature-inspired optimization algorithm, is used to optimize key parameters such as learning rate and batch size, which directly impact the performance of the model. The PSO algorithm efficiently searches the hyperparameter space by simulating the social behavior of particles in a swarm, leading to faster convergence on optimal solutions. This integration of PSO ensures that the hybrid model is both highly accurate and computationally *** TFT-GRU-PSO hybrid model is tested on actual financial data, which includes datasets of stock prices. Experimental findings demonstrate that our model surpasses conventional approaches in terms of forecasting accuracy, as evaluated by Mean Squared Error (MSE), Mean Absolute Error (MAE), and consistency in prediction. This research introduces an advanced approach to financial time series forecasting, demonstrating that hybrid deep learning models combined with optimization techniques like PSO provide a powerful tool for improving prediction in volatile and complex financial markets. © 2024 IEEE.

关键词： Deep learning

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Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

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computers, Materials & Continua 2024年第7期80卷 1457-1490页

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful *** DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS *** model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation ***,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in ***,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 *** macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are ***,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies *** research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network *** study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this *** research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

关键词： Machine-Learning Deep-Learning intrusion detection system security privacy deep neural network NSL-KDD Dataset

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Semantic2Graph: graph-based multi-modal feature fusion for action segmentation in videos

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Applied Intelligence 2024年第2期54卷 2084-2099页

作者： Zhang, Junbin Tsai, Pei-Hsuan Tsai, Meng-Hsun Department of Computer Science and Information Engineering National Cheng Kung University Tainan701 Taiwan Institute of Manufacturing Information and Systems National Cheng Kung University Tainan701 Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan

Video action segmentation have been widely applied in many fields. Most previous studies employed video-based vision models for this purpose. However, they often rely on a large receptive field, LSTM or Transformer methods to capture long-term dependencies within videos, leading to significant computational resource requirements. To address this challenge, graph-based model was proposed. However, previous graph-based models are less accurate. Hence, this study introduces a graph-structured approach named Semantic2Graph, to model long-term dependencies in videos, thereby reducing computational costs and raise the accuracy. We construct a graph structure of video at the frame-level. Temporal edges are utilized to model the temporal relations and action order within videos. Additionally, we have designed positive and negative semantic edges, accompanied by corresponding edge weights, to capture both long-term and short-term semantic relationships in video actions. Node attributes encompass a rich set of multi-modal features extracted from video content, graph structures, and label text, encompassing visual, structural, and semantic cues. To synthesize this multi-modal information effectively, we employ a graph neural network (GNN) model to fuse multi-modal features for node action label classification. Experimental results demonstrate that Semantic2Graph outperforms state-of-the-art methods in terms of performance, particularly on benchmark datasets such as GTEA and 50Salads. Multiple ablation experiments further validate the effectiveness of semantic features in enhancing model performance. Notably, the inclusion of semantic edges in Semantic2Graph allows for the cost-effective capture of long-term dependencies, affirming its utility in addressing the challenges posed by computational resource constraints in video-based vision models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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A Framework for Securing Saudi Arabian Hospital Industry: Vision-2030 Perspective

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Intelligent Automation & Soft Computing 2023年第6期36卷 2773-2786页

作者： Hosam Alhakami Abdullah Baz Mohammad Al-shareef Rajeev Kumar Alka Agrawal Raees Ahmad Khan Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955SaudiArabia Department of Computer Engineering College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Security Forces Hospital Makkah21955Saudi Arabia Department of Computer Science and Engineering Babu Banarasi Das UniversityLucknow226028Uttar PradeshIndia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia

Recent transformation of Saudi Arabian healthcare sector into a reven-ue producing one has signaled several advancements in healthcare in the *** healthcare management into Smart hospital systems is one of *** hospital management systems which are breach-proof only can be termed as effective smart hospital *** the perspective of Saudi Vision-2030,many practitioners are trying to achieve a cost-effective hospital management sys-tem by using smart *** this row,the proposed framework posits the main objectives for creating smart hospital management systems that can only be acknowledged by managing the security of healthcare data and medical ***,the proposed framework will also be helpful in gaining satisfactory rev-enue from the healthcare sector by reducing the cost and time involved in mana-ging the smart hospital *** framework is based on a hybrid approach of three key methods which include:employing the Internet of Medical Things(IoMT)and blockchain methodologies for maintaining the security and privacy of healthcare data and medical practices,and using big data analytics methodol-ogy for raising the funds and revenue by managing the bulk volume of healthcare ***,the framework will also be helpful for both the patients and the doctors,thus enabling the Kingdom of Saudi Arabia(KSA)to meet its goals of Vision-2030 by ensuring low cost,yet credible,healthcare services.

关键词： Smart healthcare healthcare industry data security

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Internet of Things and Its Applications to Smart Campus: A Systematic Literature Review

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第23期16卷 17-35页

作者： Cavus, Nadire Mrwebi, Seipati Elizabeth Ibrahim, Imran Modupeola, Temiloluwa Reeves, Albert Y. Department of Computer Information Systems Near East University Nicosia Cyprus Computer Information Systems Research and Technology Centre Near East University Nicosia Cyprus Department of Innovation and Knowledge Management Near East University Nicosia Cyprus

A smart campus is an emerging trend that will revolutionize the education system by enabling universities to improve services, and processes as well as achieve sustainability goals. With the proliferation of advanced technologies, a smart campus has emerged as an important concept that integrates technology into higher education. A smart campus takes advantage of IoT technologies to facilitate teaching and research activities. The purpose of this study is to identify the IoT technologies that are required in the development of a smart campus. This study uses a Systematic Literature Review (SLR) methodology and PRISMA processes to analyze high-quality articles on the IoT-based smart campuses from the last five years (2017-2022) as extracted from three databases like Scopus, ScienceDirect, and IEEE. The findings of the study reveal that the implementation of an IoT-based smart campus offers many advantages and benefits but also presents challenges requiring further exploration. Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further. The paper explores the many benefits and advantages that are brought by the implementation of IoT-based smart campuses. It also identifies key challenges that are presented by such implementations. Researchers, policymakers, teachers, and students can benefit from this study by gaining insights into the IoT-based smart campus © 2022,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Internet of things

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Additive Noise Model Structure Learning Based on Spatial Coordinates

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 3858-3871页

作者： Yang, Jing Lu, Ting Zhu, Youjie Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230009 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230009 China

Discovering causal relationships from a large amount of observational data is an important research direction in data mining. To address the challenges of discovering and constructing causal networks on nonlinear and high-dimensional data, this article proposes a new structural learning algorithm called spatial coordinates based (SCB). The SCB algorithm effectively discovers causal networks from large scale nonlinear data. In this article, we make three main contributions. Firstly, based on the Hilbert-Schmidt independence criterion (HSIC), we propose new independence test coefficients: spatial coordinate (SC) coefficient and conditional spatial coordinate (CSC) coefficient. We also prove that the statistical distribution of the CSC coefficient follows a standard normal distribution. Secondly, using the statistical distribution of the CSC coefficient, we redefine the correlation of variables and combine it with local learning to propose the SCB algorithm. Finally, we demonstrate the effectiveness of the algorithm through experiments on data generated from various nonlinear function dependencies. Compared to existing algorithms, the causal network models constructed using the SCB algorithm exhibit significant improvements in accuracy and time performance. The effectiveness of the proposed method is further validated on real power plant data. © 2020 IEEE.

关键词： Additive noise

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Methodology and Infrastructure for Teaching Operating systems Courses for Large Number of University Students 18

Methodology and Infrastructure for Teaching Operating System...

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18th Iberian Conference on information systems and Technologies, CISTI 2023

作者： Zemanek, Petr Muzikar, Zdenek CTU in Prague Faculty of Information Technology Department of Computer Systems Prague Czech Republic

ISBN: (纸本)9789893347928

The article shares experience with teaching operating systems courses (of various complexity) for large number (up to one thousand) students. The nature of operating systems requires specific methods of lab environment/setup, lab exercises development and design of knowledge tests and final exam requirements. Automation of teaching processes is needed while maintaining quality of knowledge transfer and protecting reputation of the University. The article covers our experience with teaching courses covering UNIX/LINUX basics, administration of UNIX/LINUX operating systems, Operating systems (classical course) and LINUX Kernel Programming. We discuss infrastructure that we designed, and we have been successfully using for several years. © 2023 ITMA.

关键词： Virtualization

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