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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sahoo, Girija Shankar Hannah Jessie Rani, R. Goyal, Manish Kumar Mohammed, Vazeer Ali Jana, D Little Femilin Wasatkar, Namrata N. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Lewis University Illinois Department of Computer & Information System United States Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

This paper proposes the use of adaptive algorithms for real-time data analysis. Adaptive algorithms are a set of practical methods used for acting information-driven optimization and studying. They have a wide variety of applications and may be implemented to a selection of facts analysis obligations. This paper offers an overview of existing adaptive algorithms and descriptions of several new programs in the area of actual-time facts analysis. In particular, the paper discusses the notion of adaptive thresholding, which allows for practical real-time evaluation of large datasets. The paper also provides a novel technique for making use of these algorithms in an online learning environment. Results from simulations illustrate the ability of the proposed method in real-international packages. Sooner or later, the paper presents numerous key lessons discovered and affords recommendations for future research and packages in this area. © 2024 IEEE.

关键词： Adaptive algorithms

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3D Virtual Reality Application with Design Approach for Introduction of Istano Basa Pagaruyung Tourist Attractions 23

3D Virtual Reality Application with Design Approach for Intr...

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11th International Conference on computer and Communications Management, ICCCM 2023

作者： Adrian, Qadhli Jafar Hidayat, Robby Rahmat Pasha, Donaya Maharini, Anggita Septia Lubis, Muharman Lubis, Arif Ridho Department of Information System Universitas Teknokrat Indonesia Indonesia Department of Information Technology Universitas Teknokrat Indonesia Indonesia Department of Computer Engineering and Informatics Politeknik Negeri Medan Indonesia

ISBN: (纸本)9798400707735

This research is about the development of proper media for the tourist attraction, which in this case related to the Istano Basa Pagaruyung in order to increase the engagement during Covid-19 pandemic, which is the Android-based. It aims to produce interactivity and innovation product service by using Virtual Reality (VR) technology through design approach method as cognitive and practical process. For the purpose of testing, there are three aspects that were evaluated namely the functional conformity, the usability and the portability. In the first aspect, the results indicated almost 100% successful presentation, while the usability aspect provide the presentation value of 86.7% and the last aspects show zero errors result. Thus, it can conclude that the application can be one alternative solution to promote tourist attraction in the midst of the Covid-19 pandemic in term of marketing or learning mechanism. It also presents the useful practice on the utilization of design approach that can be aligned with multimedia development life cycle (MDLC) to have non-linear and iterative process in order to redefine the problem for designing the alternative solution © 2023 ACM.

关键词： Virtual reality

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Quadrature Demodulation as a Technique to Measure Signal Parameters with Increased Accuracy 34

Quadrature Demodulation as a Technique to Measure Signal Par...

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34th International Scientific Symposium Metrology and Metrology Assurance, MMA 2024

作者： Krylovich, Anna A. Podobuev, Sergey A. Loginov, Vadim A. Zolkin, Dmitry S. Serov, Nikolay A. Serov, Andrey N. National Research University 'Moscow Power Engineering Institute' Diagnostic Information Technologies Department Moscow Russia National Research University 'Moscow Power Engeneering Institute' Computer Engineering Department Moscow Russia Division for Design of Information Retrieval System Moscow Russia

ISBN: (纸本)9798350352832

To solve modern problems related to spectrum measurement, the most common use of discrete Fourier transform algorithms. The main problem of using these algorithms is associated with the so-called 'spectrum leakage' effect, the occurrence of which limits the maximum achievable accuracy from the point of view of the methodological component of the error. An alternative solution to the spectrum measurement problem is to use a quadrature demodulator. The approach itself has great potential in terms of the final accuracy achieved. The use of additional modifications discussed in the paper can further improve it. The paper discusses the advantages and disadvantages of all the considered modifications, and obtains analytical expressions that allow us to estimate the final achievable error. An assessment of possible influencing quantities was carried out and their influence on the measurement errors of amplitude, initial phase and frequency was assessed. Considerations are given for developing a simulation model of such a demodulator and its modifications under consideration. A ready-made model which is performed in Simulink environment is presented. The correctness of the obtained analytical relationships was checked by comparing the results of simulation modeling and analytical calculations. © 2024 IEEE.

关键词： Demodulators

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Improving Arabic Text Classification Using P-Stemmer

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Recent Advances in computer Science and Communications 2022年第3期15卷 404-411页

作者： Kanan, Tarek Hawashin, Bilal Alzubi, Shadi Almaita, Eyad Alkhatib, Ahmad Maria, Khulood Abu Elbes, Mohammed Department of Computer Science AlZaytoonah University Amman Jordan Department of Computer Information System AlZaytoonah University Amman Jordan Department of Power and Mechatronics Engineering Tafila Technical University Tafilah Jordan

Introduction: Stemming is an important preprocessing step in text classification, and could contribute to increasing text classification accuracy. Although many works have proposed stemmers for the English language, few stemmers have been proposed for Arabic text. Arabic language has gained increasing attention in the previous decades and the need to further improve Arabic text classification. Methods: This work combined the use of the recently proposed P-stemmer with various classifiers to find the optimal classifier for the P-stemmer in terms of Arabic text classification. As part of this work, a synthesized dataset was collected. Results: The previous experiments show that the use of P-stemmer has a positive effect on classification. The degree of improvement is classifier-dependent, which is reasonable as classifiers vary in their methodologies. Moreover, the experiments show that the best classifier with the P-Stemmer is NB. This is an interesting result as this classifier is well-known for its fast learning and classification time. Discussion: First, the continuous improvement of the P-stemmer by more optimization steps is necessary to further improve the Arabic text categorization. This can be made by combining more classifiers with the stemmer, by optimizing the other natural language processing steps, and by improving the set of stemming rules. Second, the lack of sufficient Arabic datasets, especially large ones, is still an issue. Conclusion: In this work, an improved P-stemmer was proposed by combining its use with various classifiers. In order to evaluate its performance, and due to the lack of Arabic datasets, a novel Arabic dataset was synthesized from various online news pages. Next, the P-stemmer was combined with Naïve Bayes, Random Forest, Support Vector Machines, K-Nearest Neighbor, and K-Star. © 2022 Bentham Science Publishers.

关键词： Decision trees

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A Hierarchical Traffic Sign Image Recognition Algorithm Keeping the Local Topology 7

A Hierarchical Traffic Sign Image Recognition Algorithm Keep...

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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Liu, Longhui Gong, Wenhui Wang, Wenjie Zeng, Zhiqiang Li, Wei Xiamen Global Navigation Satellite System Development & Application Co. Ltd. Xiamen China School Of Computer And Information Engineering Xiamen University Of Technology Xiamen China

ISBN: (纸本)9798350361445

Traffic sign recognition is crucial in enabling intelligent driving. This paper proposes a robust hierarchical traffic sign image recognition algorithm that maintains local topology. The algorithm is divided into two matching levels: rough matching and fine matching. The multi-scale image's Oriented Fast and Rotated BRIEF (ORB) [1] are extracted in coarse matching. Hamming distance is adopted to measure the similarity of feature points to execute rough and fast matching. In precise matching, the matched candidate feature points are further filtered using the number of features of surrounding points, that is, the Shape Context [2] feature, to achieve precise matching. This hierarchical matching method maintains the local topological characteristics of the image while performing robust and fast matching. In the experiment, the data set provided by Changsha University of Science and Technology was used to assess the performance of the algorithm presented in this article. © 2024 IEEE.

关键词： Traffic signs

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ARiViT: attention-based residual-integrated vision transformer for noisy brain medical image classification

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European Physical Journal Plus 2024年第5期139卷 1-13页

作者： Hameed, Madiha Zameer, Aneela Khan, Saddam Hussain Raja, Muhammad Asif Zahoor Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Pakistan Department of Computer System Engineering The University of Engineering and Applied Science (UEAS) Swat Pakistan Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan

Brain tumor detection in medical image processing presents a formidable challenge due to the complex behavior exhibited by these tumors. Their intricate nature arises from a variety of shapes and textures within brain tumor images, exacerbated by their diverse cellular origins. This complexity is further compounded by the presence of noisy images, which add an additional layer of difficulty. The overlapping image intensities in tumor and non-tumor areas pose a significant hurdle in extracting meaningful insights from raw data using predictive models. In response to these challenges, we introduce ARiViT, a novel framework based on residual learning. ARiViT merges vision transformers, convolutions, and adversarial learning to tackle the complexities of brain tumor detection. ARiViT’s core generator integrates RiT blocks, merging residual convolutions and transformers, with residual connections, channel compression, and strategic weight sharing for enhanced capabilities and computational efficiency. ARiViT demonstrates exceptional robustness in handling noisy images and adapts well to various modality setups. Our systematic approach involves dataset splitting into training, testing, and validation sets, maintaining distributions of 80:20 and subsequently 70:30, ensuring rigorous evaluation. Additionally, our data preprocessing strategies are tailored to handle noisy and low-quality images, as well as those with unusual tumor shapes. The GAN architecture embedded in ARiViT effectively manages the complexities inherent in such image variations, ensuring reliable tumor detection even in challenging scenarios. Through exhaustive comparative evaluations, ARiViT proves its superiority over existing CNN and transformer techniques, as evidenced by both qualitative and quantitative analyses. Our efforts culminate in remarkable achievements, including an F1-score of 98.09%, establishing a pioneering solution in the demanding field of brain tumor detection. © The Author(s), under

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Analyzing Employee Retention Factors using Machine Learning 4

Analyzing Employee Retention Factors using Machine Learning

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Rahman, Moshiur Islam, Md Rashedul Bala, Partho Sattar, Abdus Daffodil International University Department of Management Information System Daffodil Smart City Dhaka Bangladesh Daffodil Institute of Information and Technology Department of Computer Science and Engineering Dhanmondi Dhaka Bangladesh Bangabandhu Sheikh Mujibur Rahman Science and Technology University Department of Computer Science and Engineering Dhaka8100 Bangladesh Daffodil International University Department of Computer Science and Engineering Daffodil Smart City Dhaka Bangladesh

ISBN: (纸本)9798350343670

This research proposal aims to employ machine learning techniques to analyze employee retention factors in Software Companies, recognizing its crucial role in organizational success and the potential costs of high turnover rates. Through Watson Analytics' advanced analytics capabilities, the study seeks to identify key factors contributing to employee attrition and retention, culminating in the development of a predictive model using machine learning algorithms. The outcomes are expected to include actionable recommendations to improve retention strategies, insights into the relative importance of different factors, and the creation of a data- driven, proactive talent management approach for Software Companies. By empowering organizations to retain top talent and fostering a positive work environment, this research envisions a transformative impact on long-term success and performance. © 2024 IEEE.

关键词： Artificial Intelligence Data Science Deep Learning Machine Learning Monitoring Smartphone Sensors

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Enhanced Crow Search with Deep Learning-Based Cyberattack Detection in SDN-IoT Environment

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Intelligent Automation & Soft Computing 2023年第6期36卷 3157-3173页

作者： Abdelwahed Motwakel Fadwa Alrowais Khaled Tarmissi Radwa Marzouk Abdullah Mohamed Abu Sarwar Zamani Ishfaq Yaseen Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMeccaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharj16436Saudi Arabia

The paradigm shift towards the Internet of Things(IoT)phe-nomenon and the rise of edge-computing models provide massive poten-tial for several upcoming IoT applications like smart grid,smart energy,smart home,smart health and smart transportation ***,it also provides a sequence of novel cyber-security *** IoT networks provide several advantages,the heterogeneous nature of the network and the wide connectivity of the devices make the network easy for *** result in financial loss and data breaches for organizations and ***,it becomes crucial to secure the IoT environment from such *** this motivation,the current study introduces an effectual Enhanced Crow Search Algorithm with Deep Learning-Driven Cyberattack Detection(ECSADL-CAD)model for the Software-Defined Networking(SDN)-enabled IoT *** presented ECSADL-CAD approach aims to identify and classify the cyberattacks in the SDN-enabled IoT *** attain this,the ECSADL-CAD model initially pre-processes the *** the presented ECSADL-CAD model,the Reinforced Deep Belief Network(RDBN)model is employed for attack *** last,the ECSA-based hyperparameter tuning process gets executed to boost the overall classification outcomes.A series of simulations were conducted to validate the improved outcomes of the proposed ECSADL-CAD *** experimental outcomes confirmed the superiority of the proposed ECSADL-CAD model over other existing methodologies.

关键词： Software defined networks artificial intelligence cybersecurity deep learning internet of things

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Acute Lymphoblastic Leukemia Detection Using DenseNet Model from Microscopic Blood Smear Images

Acute Lymphoblastic Leukemia Detection Using DenseNet Model ...

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International Conference on Data Science and Network engineering, ICDSNE 2023

作者： Prasath, Navamani Jasthi, Monika Saikia, Rabul Loganathan, Muralidaran Devi, Salam Shuleenda Department of Electronics and Communication Engineering NIT Meghalaya Meghalaya Shillong793003 India Department of Computer Science and Information System Engineering St. Joseph University Dae Es Salaam Tanzania United Republic of

ISBN: (纸本)9789819967544

Acute lymphoblastic leukemia (ALL) is a cancerous condition that affects the bone marrow and blood. It is a fast developing illness that, if not identified and treated as soon as possible, could be fatal. ALL is often identified by looking at blood and bone marrow smears under a microscope. Leukemia can be detected and classified using detailed cytochemical tests. However, these procedures are expensive, time-consuming, and dependent on the knowledge and skills of the specialists involved. Using image processing techniques that examine microscopic blood smear images to search for the leukemic cells, leukemia can be detected. These methods are simple, quick, cost-effective, and unaffected by the judgments of experts. The suggested study describes a computer-aided diagnosis method that uses deep convolutional neural networks (CNNs) that have already been trained to compare leukemia images to normal images. The public dataset ALL-IDB 2 was used for the proposed research. The study uses the pre-trained model DenseNet-201 for performing the classification. With the DenseNet201 pre-trained networks employed in the study for the ALL_IDB2 dataset, a classification accuracy of 94.6% is achieved. In all of the classifications carried out, optimization strategies such as cross-validation, fine-tuning, and real-time augmentation are also compared. Also use Pre-trained series models like ResNet-50, VGG-19, Inceptionv3, MobileNet-v2, Xceptionv3, and VGG-16 for performing the comparison. The experimental result gives an improvement in accuracy (17.76, 10.6, and 13.2%) in comparison to the other approaches namely, residual neural network, customized combined CNN, and conVNet neural network, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computer aided diagnosis

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A lightweight point cloud analysis network based on graph walking 8

A lightweight point cloud analysis network based on graph wa...

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8th International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2023

作者： Li, Wenju Ji, Qianqian Sha, Liye Cui, Liu Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China Shanghai Precision Dosing & Weighing System Co Shanghai China

ISBN: (纸本)9798350369564

Based on the irregularity and permutation invariance of point clouds, graph neural networks have been widely used in 3D point cloud analysis tasks in recent years. However, these works suffer from insufficient feature extraction at long distances and high computational resource overhead. To this end, this paper proposes a graph walk based lightweight network for 3D point cloud analysis tasks. Specifically, after constructing the nearest neighbor graph in the downsampled point cloud, the MLP module in the original network was replaced by multiple nonparametric components to reduce the number of parameters of the model. Then, the graph walking mechanism was used to extract the long-distance features and geometric structure features of the point cloud. Finally, before point classification and segmentation, the residual pooling module is used to fuse the features to obtain the global features. Compared with the benchmark network DGCNN, the classification accuracy on the ScanObjectNN dataset is improved by 4.2%, the segmentation accuracy on the ShapeNetPart dataset is improved by 1.1%, and the number of model parameters is reduced by 33.3%. Experiments show that the design method has obvious effect on 3D point cloud analysis tasks. © 2023 IEEE.

关键词： Graph neural networks

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