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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Shree, Ritu Verma, Pradeep Kumar Kavitha, R. Vivekananda Global University Department of Computer Science & Application Jaipur India College of Engineering Teerthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Millimeter wave beamforming for large MIMO in 6G verbal exchange systems is a promising era that could dramatically improve the overall performance and spectral efficiency of 6G networks. This generation entails the use of more than one antenna and antenna array to direct and recognize the electromagnetic power in a specific path. One of the core variations of millimeter wave beamforming in comparison with traditional more than one-antenna approaches is using fairly directional beams. This provides massive blessings in the communique link, together with improved bandwidth, improved user enjoyment, and stepped forward protection. Similarly, the use of a couple of antenna factors in this technology allows for beamforming, which could substantially lessen interference and enhance spectrum performance. Ultimately, the deployment of this era additionally calls for effective signal processing techniques, along with coding and modulation that can similarly boost the overall gadget capability. In conclusion, millimeter wave beamforming for huge MIMO in 6G verbal exchange systems can load up a huge range of possibilities and blessings, which could substantially enhance the user experience and warfare congestion in 6G networks. © 2024 IEEE.

关键词： Antenna Beamforming Dramatically Millimeter Networks Traditional

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A Task Scheduling Approach for Optimizing 5G Edge Network Performance Through Cloud-Edge Collaborative Computing

A Task Scheduling Approach for Optimizing 5G Edge Network Pe...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Shanthi, M.B. Rao, Vaishali K. P. Sudhakar, K.N. Parameshachari, B.D. Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Cmr Institute of Technology Department of Information Science and Engineering Bengaluru India Rv University Department of Computer Science and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

This document discusses various techniques that are crucial for 5G mobile communication. These techniques include high-frequency transmission, direct communication, device-to-device (D2D) communication and massive multiple input and multiple-output (MIMO) technologies. The document provides an overview of each technology, including its benefits and limitations. In addition to these techniques, the document also covers mobile edge computing (MEC), which involves distributing computer resources over mobile communication networks. The document outlines eight potential uses of MEC. Lastly, the document proposes a cloud-edge collaborative computing job scheduling system. The proposed technique aims to dynamically assign computer resources to edge devices based on network conditions and current workloads while considering network latency, computation capability, and energy use. Performance scores for the Cloud Only plan range from 4.0 to 4.5, which shows that the plan is very effective. Lower scores from 1.8 to 3.0 obtained by the Edge Only approach imply that its performance is constrained. © 2023 IEEE.

关键词： 5G technology Cloud Edge Resources Device-To-Device Communication Direct Communication High-Frequency Transmission Low Latency Massive MIMO Mobile Edge Computing Multiple-Carrier Techniques Task Scheduling

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Automizing computer Vision Systems with Deep Learning Techniques 3

Automizing Computer Vision Systems with Deep Learning Techni...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Saraswat, Pankaj Pavan, P.S. Chaudhary, Chetan Sanskriti University Department of Computer Science and Engineering Uttar Pradesh Mathura India School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350354171

Deep getting-to-know techniques have been used to automate PC imaginative and prescient systems during the last few years with terrific fulfillment. Deep mastering is a subset of the system gaining knowledge of that uses synthetic neural networks, a set of synthetic neurons that paintings collectively to simulate the biological neurons of a human mind. Through using deep learning, a synthetic neural network is skilled to understand patterns from pics or videos, which can then be used to understand gadgets in pictures or to song changes over time. Because of the big potential for automation in computer imaginative and prescient structures, deep getting-to-know techniques are being actively researched and developed by way of each instructional and industrial team. Presently, deep getting-to-know networks are used in a spread of PC imaginative and prescient programs, which include facial reputation, item recognition, and video object *** to the modern methods, studies into the development of extra-effective techniques are ongoing. This research is focused on growing algorithms that could perform nicely in one-of-a-kind environments, as well as the development of new architectures and different parameters that could accelerate schooling overall performance. A lot of those new processes involve the use of statistics-driven fashions along with convolutional neural networks. With the aid of the use of deep studying techniques to automate PC imaginative and prescient structures, the capacity for automated programs in fields that include surveillance, medical imaging, and self-reliant cars is massive. © 2024 IEEE.

关键词： Convolutional neural networks

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Envisioning Educational Success Through Advanced Analytics and Intelligent Performance Prediction 10

Envisioning Educational Success Through Advanced Analytics a...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Sureshkumar, T. Charanya, J. Kumaresan, T. Rajeshkumar, G. Kavin Kumar, P. Anuj, B. Nehru Institute of Technology Department of Information Technology Coimbatore641 105 India Department of Computer Science and Engineering Erode638 401 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam638 401 India Bannari Amman Institute of Technology Dept of Cse Sathyamangalam India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India

ISBN: (纸本)9798350353068

The accurate prediction of students academic success holds significance in enhancing educational outcomes. The study focuses on overcoming the challenge of assessing whether a student will successfully pass the final exam, taking into account a range of socio-economic and academic factors. Every student's journey is different, and it is influenced by various factors such as their socioeconomic background, academic abilities, and personal experiences. To evaluate their performance, we use various machine learning algorithms such as KNN, Naive Bayes, SVM, AdaBoost, and Naive Bayes - and assess their performance using key metrics such as ROC curve, confusion matrix, precision, recall, and F1 score. The F1 score offers a balanced assessment of precision and recall, offering insights into overall model accuracy. Precision and recall focus specifically on the model's ability to correctly identify positive instances and capture all actual positive instances, respectively. ROC curves demonstrate the balance between the rates of correctly identifying true positives and incorrectly identifying false positives, allowing for a nuanced assessment of classifier performance. The confusion matrix further breaks down true positives, true negatives, false positives, and false negatives, enhancing our understanding of algorithmic effectiveness. Through a comprehensive comparison of these classification methods, our goal is to identify the most accurate approach for predicting student outcomes, thus contributing valuable insights to educational institutions and policymakers aiming to implement effective strategies for student success and academic improvement. © 2024 IEEE.

关键词： Students

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Investigating the Effects of Power System Harmonics on EMC in Industrial Environments 15

Investigating the Effects of Power System Harmonics on EMC i...

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

作者： Raghavendra Rao, P.S. Sahoo, Girija Shankar Saraswat, Bhawna School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India

ISBN: (纸本)9798350370249

Electricity gadget harmonics are one of the most prominent sources of electromagnetic compatibility (EMC) issues in industrial environments. Harmonics are acknowledged to create currents or voltages which can be out of segment with the essential voltage sign, resulting in extended losses in power transmission lines, inductive devices, and nonlinear loads. Additionally, harmonics can bring about tremendous interference with different electrical systems, and might doubtlessly purpose degradation of the strength machine's potential to transfer energy. This examine investigates the outcomes of energy gadget harmonics on EMC in business environments. A gadget of components and equipment related to power transmission and distribution, energy conversion, strength great, and protection and reliability is situation to detailed analysis of the harmonics gift. The analysis of the harmonic parameters and electrical electricity switch traits might be used to evaluate the capacity risks of EMC issues associated with harmonics, as well as to formulate capacity manage strategies that could be hired to mitigate the consequences of strength gadget harmonics. The important outcome of this research is anticipated to offer deeper perception into the have an impact on of power machine harmonics on EMC in industrial environments and perceive capability manipulate strategies to reduce the associated dangers. © 2024 IEEE.

关键词： Acknowledged Conversion Degradation Harmonics Prominent Protection Transmission

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Distance Estimation in Video using Machine Learning 3

Distance Estimation in Video using Machine Learning

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3rd International Conference on Smart Data Intelligence, ICSMDI 2023

作者： Suresha, D. Aravinda, C.V. Bhatnagar, Roheet Information Science and Engineering Aj Institute of Engieering & Technology Karnataka Mangalore India Computer Science and Engineering N.M.A.M. Institute of Technology Karkala Karnataka India Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9781665464871

In a video, distance estimate refers to calculating the distance between an object and the camera. The camera records the live video of a person walking in front of it. Live video is collected when a human stands in front of the camera and begins to walk in front of it. A sequence of video frames is derived from the collected live footage. These frames are handled separately. Each frame is subjected to a face detection method. The detected face is surrounded by a rectangle. The rectangle created around the detected face is used to calculate height and breadth. This is known as the perspective width. The focal length is calculated using the perspective width. The proposed system is using the focal length to determine distance once it has been calculated. The user can now walk in front of the system, which is now ready for distance estimation. The basic goal is to recognize a moving face and calculate its distance from the camera. In the realm of research, distance estimate is useful. For execution, the initiative makes use of cutting-edge technologies such as machine learning. © 2023 IEEE.

关键词： Cameras

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The role of data governance in a high-level approach of data migration to open data

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Business information Review 2024年第3期41卷 94-103页

作者： Yulfitri, Alivia Sensuse, Dana Indra Sumirat Hidayat, Deden Suryono, Ryan Randy Kautsarina Purwaningsih, Erisva Hakiki Prabuwono, Anton Satria Faculty of Computer Science Universitas Indonesia Depok Indonesia Faculty of Computer Science Universitas Esa Unggul West Jakarta Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency (BRIN) Central Jakarta and Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Lampung Indonesia Information and Communications Technology Human Resources Development Agency Ministry of Communication and Information Technology of the Republic of Indonesia Jakarta Central Jakarta Indonesia Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah Saudi Arabia

Data quality issues are still a challenge in data migration. Existing methods mainly focus on technical issues. However, the problem of data quality is not only a technical problem, but the main one is the non-technical aspect. This paper aims to identify the role of data governance in the data migration process. The qualitative research method uses two primary references, namely the high-level data migration approach from Kazimir et al. and the DAMA-DMBOK framework. Both are highly compatible with data migration processes focusing on data quality and referencing governance roles. The case study was conducted in a central government working on data migration to the new One Data application. The results show that eight data governance roles are involved in 20 migration processes (65%). Non-technical activities related to data quality involve more than half of the data migration process. This paper proposes the role of data governance for non-technical aspects of the migration process. This paper presents a case study of the high-level data migration process and how data governance comes into play. This research can help the government and practitioners overcome problems that often occur in data migration, especially related to data quality. © The Author(s) 2024.

关键词： DAMA-DMBOK framework data governance data migration Indonesia’s one data Kazimir et al.’s approach open data

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Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

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computers, Materials & Continua 2022年第10期73卷 865-881页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Abdelhameed Ibrahim Said H.Abd Elkhalik Shady Y.El-Mashad Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

The design of an antenna requires a careful selection of its parameters to retain the desired ***,this task is time-consuming when the traditional approaches are employed,which represents a significant *** the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended *** this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial *** proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep *** optimized network is used to retrieve the metamaterial bandwidth given a set of *** addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

关键词： Metamaterial antenna long short term memory(LSTM) guided whale optimization algorithm(Guided WOA) adaptive dynamic particle swarm algorithm(AD-PSO)

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A Comprehensive Analysis of Various Deep Learning Based Multi Class Plant Disease Classification Techniques 1

A Comprehensive Analysis of Various Deep Learning Based Mult...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Kala, Deepika Punia, Divya Sikka, Geeta Sikka, Kushagra National Institute of Technology Delhi Department of Computer Science and Engineering Delhi India National Institute of Technology Delhi Department of Electronics and Communication Engineering Delhi India University of Florida Gainesville Department of Information Science and Engineering Gainesville United States

ISBN: (纸本)9798350365016

In modern agriculture, deep learning has become increasingly essential to identify plant diseases in the field of plant disease identification with leaf images, where convolutional neural networks (CNN) are garnering a lot of interest. Plant disease iden-tification and prevention are essential for producing healthy crops and maintaining farmers' livelihoods. This study uses leaf images to examine the effectiveness of deep learning (DL) algorithms for multiclass plant leaf disease classification. The training data was expanded using data augmentation techniques, which eliminated the need for further image collecting. This paper uses three models namely InceptionV3, VGG16, and EfficientNetB0 that have been compared using the plantVillage data set. The three models that achieved the highest accuracy were EfficientNetBO (98.51 %), InceptionV3 (97.78%), and VGG16 (95.37%) for plant detection, and EfficientNetBO (97.58%), InceptionV3 (96.83%), and VGG 16 (94.35 %) for plant detection. Notably, EfficientNetBO performed better for disease classification than the other two algorithms. Farmers are able to take prompt action to stop the disease's spread and reduce crop losses since the technology can deliver fast and precise results. © 2024 IEEE.

关键词： Convolutional neural networks

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Dysgraphia Detection Using Machine Learning-Based Techniques: A Survey 2nd

Dysgraphia Detection Using Machine Learning-Based Techniques...

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2nd International Conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

作者： Agarwal, Basant Jain, Sonal Bansal, Priyal Shrivastava, Sanatan Mohan, Navyug Department of Computer Science and Engineering Central University of Rajasthan Ajmer India PG Department of Computer Science and Technology Sardar Patel University Vallabh Vidhyanagar Anand India Department of Computer Science and Engineering Indian Institute of Information Technology Kota Kota India

ISBN: (纸本)9789819919086

Dysgraphia is a handwriting disorder which affects the writing abilities of an individual. Currently there is no cure for this disorder but even the diagnosis is difficult. This paper summarises the current research on detection of Dysgraphia through machine learning. Researchers around the globe have researched this topic and have proposed different solutions for the same. The types of Dysgraphia and their symptoms are also discussed in this paper. Different accuracies were achieved by using a variety of Machine Learning algorithms like Support Vector Machine, AdaBoost, K-Means Clustering, Random Forest, Convolutional Neural Networks, etc. Another aim of this paper is to spread awareness about the problem of Dysgraphia and its implications on society. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Adaptive boosting

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