2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)最新文献_第5页

5G Network Slicing Algorithm Development using Bagging based-Gaussian Naive Bayes 基于Bagging -高斯朴素贝叶斯的5G网络切片算法开发

2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2023-04-05 DOI: 10.1109/ICEEICT56924.2023.10157595

A. Vijayalakshmi, E. Abishek B, Abdulsamath G, S. N, Mohamed Absar M, Arul Stephen. C

{"title":"5G Network Slicing Algorithm Development using Bagging based-Gaussian Naive Bayes","authors":"A. Vijayalakshmi, E. Abishek B, Abdulsamath G, S. N, Mohamed Absar M, Arul Stephen. C","doi":"10.1109/ICEEICT56924.2023.10157595","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10157595","url":null,"abstract":"Existing cellular communications and future communication networks requires very low latency, high reliability standards, increased capacity, enhanced security, and efficient user communication. The ability to accommodate several independent devices is a feature that mobile operators are seeking for a programmable solution, comparable functional networks technical foundation. Through the use of the Network Slicing concept, 5G networks enable end-to-end deployment of network resources (NS). Due to the surge in traffic and the acceleration of 5G network performance, emerging communication networks will demand data-driven strategic planning. This paper has to implement machine learning based network slicing algorithm to divide 5G network IoT devices into effective network slices such as eMBB, mMTC, URLLC for the traffic. The GNB and B-GNB algorithms are used to classify the usecase devices under the three network slices. This work developed bagging integrated with GNB algorithm and its performance metrics have been analysed. The B-GNB algorithm works well for prediction of best slice and strategic recommendations even there is network interruption, be able to predict the best network slice and implement strategic recommendations. The performance metrics such as sensitivity, F-score, precision and accuracy have also been analyzed. The comparative analysis shows B-GNB classify the slices with 86% of accuracy.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132495460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Meme Expressive Classification in Multimodal State with Feature Extraction in Deep Learning 基于深度学习特征提取的多模态模因表达分类

2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2023-04-05 DOI: 10.1109/ICEEICT56924.2023.10157066

A. Barveen, S. Geetha, Mohamad Faizal

{"title":"Meme Expressive Classification in Multimodal State with Feature Extraction in Deep Learning","authors":"A. Barveen, S. Geetha, Mohamad Faizal","doi":"10.1109/ICEEICT56924.2023.10157066","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10157066","url":null,"abstract":"Memes are a socially interactive way to communicate online. Memes are used by users to communicate with one another on social networking sites and other forums. Memes essentially focus on speech recognition and image macros. While a meme is being created, it focuses on the semiotic type of resources that the internet community interprets with other resources, which facilitates the interaction among the internet and meme creators. Memes recreate based on various approaches, which fall under various acts such as existing speech acts. Based on the expressive face with captioned short texts, even the short text is exaggerated. Every year, meme mimicking applications are created that allow users to use the imitated meme expressions. Memes represent the shared texts of the younger generations on various social platforms. The classifications of sentiment based on the various memetic expressions are the most efficient way to analyse those feelings and emotions. HOG feature extraction allows the images to be segmented into blocks of smaller size by using a single feature vector for dimension, which characterizes the local object appearances to characterize the meme classification. The existence of specific characteristics, including such edges, angles, or patterns, is then analyzed by combining HOG features using multi-feature analysis on patches. Based upon the classification methodology, it classifies the sentiments, which tend to improve the learning process in an efficient manner. By combining a deep learning approach with a recurrent neural network, the extended LSTM-RNN can identify subtle nuances in memes, allowing for more accurate and detailed meme classification. This proposed method effectively evaluates several classification techniques, including CNN and Extended LSTM-RNN for meme image characterization. Through training and validation, Extended LSTM-RNN achieved 0.98% accuracy with better performance than CNN.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132169549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low power and high speed level translator using Widlar topology 采用Widlar拓扑结构的低功耗和高速转换器

2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2023-04-05 DOI: 10.1109/ICEEICT56924.2023.10157495

Nithia Shree A C, M. R, Arul A, S. Ramesh

{"title":"Low power and high speed level translator using Widlar topology","authors":"Nithia Shree A C, M. R, Arul A, S. Ramesh","doi":"10.1109/ICEEICT56924.2023.10157495","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10157495","url":null,"abstract":"This study examines two different forms of energy-saving and rapid voltage level changers are designed in this research. This article provides comprehensive information on logic down shifters and logic up shifter. The placement of level shifter plays crucial role, the low to high level shifters requires single supply voltage whereas high to low level shifter requires dual supply voltage. Level shifters have been developed using gpdk 45nm technology. The level changer design described in this paper can transform input voltages from sub-threshold levels to the desired voltage supply. The level shifter can convert high voltage (VVDH) to low voltage (VVDL) and vice versa. The level shifter designed here using Widlar current mirror instead of Wilson current mirror. Due to the development of highly efficient and low power consumption application, it is important to manage a complex circuit with minimal power consumption to achieve, the best method for lowering system-level power usage is multi supply voltage domain. For interconnection of ICs and to avoid static current and to accommodate supply voltage configurations, level translators (LSs) must be used. The designed level shifters are simulated using Cadence tool","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116203779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SMS Spam Classification and Through Recurrent Neural Network (LSTM) model 基于递归神经网络(LSTM)模型的短信垃圾邮件分类

2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2023-04-05 DOI: 10.1109/ICEEICT56924.2023.10157514

J. Rajasekhar, T. Hemanth, Anjuman Sk

{"title":"SMS Spam Classification and Through Recurrent Neural Network (LSTM) model","authors":"J. Rajasekhar, T. Hemanth, Anjuman Sk","doi":"10.1109/ICEEICT56924.2023.10157514","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10157514","url":null,"abstract":"Short messaging service (SMS) spam is the unwanted messages delivered to the inbox of mobile devices from spammers. Service providers are worried about these spam messages as their clients get dissatisfied with services due to the spam data reaching on their mobile phone. There are most of the service providers has given facility Do Not Disturb (DND) activation for their clients to save them from most of the spam messages. Even though the spam messages are not controlled fully, the delivery of such messages are unstoppable. To overcome this issue extensive research has been done. Artificial intelligence made it possible with extensive learning model and accuracy of detection. This paper is proposed to classify short messages as spam or ham based on a deep learning model. In this paper, the spam detection through Recurrent Neural Network (RNN) model, in specific Long Short Term Memory (LSTM) model is used. The dataset used for this study is extracted from Grumbletext website and it has a total 425 short messages with ‘Ham’ and ‘spam’. The LSTM model classified the SMS dataset effectively with the learning model. Experimental study showed that the model has achieved an accuracy of 88.33% accuracy on SMS spam classification with the LSTM model.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116577825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of Microstrip Patch Antenna At 3.5 GHz Frequency Using FEKO Simulation 基于FEKO仿真的3.5 GHz微带贴片天线设计

2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2023-04-05 DOI: 10.1109/ICEEICT56924.2023.10157700

Ramakrishna Ch, Krishna Chaitnaya Varma A, Rangarao Orugu, V. V. S. S. Ch, K. M, Venkateswara Rao Ch

{"title":"Design of Microstrip Patch Antenna At 3.5 GHz Frequency Using FEKO Simulation","authors":"Ramakrishna Ch, Krishna Chaitnaya Varma A, Rangarao Orugu, V. V. S. S. Ch, K. M, Venkateswara Rao Ch","doi":"10.1109/ICEEICT56924.2023.10157700","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10157700","url":null,"abstract":"A fundamental microstrip patch antenna is made up of a ground and a metallic patch separated by a dielectric layer known as the substrate. These antennas are commonly used in communications, especially in military and civil applications. This study uses FEKO simulation software to design and simulate a microstrip patch antenna that operates at 3.5 GHz. The design process involves selecting an appropriate substrate material and its thickness, determining the patch dimensions, selecting the ground plane dimensions, creating a simulation model in FEKO, and analysing the performance in terms of directivity, radiation pattern, and gain. Several challenges involved in the design process are discussed, including substrate material selection, patch dimensions, ground plane dimensions, simulation accuracy, optimization, and fabrication tolerance. These challenges are addressed through careful consideration of the antenna design parameters and the use of FEKO simulation software to accurately model and simulate the antenna's performance. The simulation results demonstrate that the designed microstrip patch antenna at 3.5 GHz frequency meets the desired performance specifications. The antenna has a return loss of −20 dB, a radiation pattern that is nearly omnidirectional, and a gain of 2.5 dBi. The simulation results demonstrate the effectiveness of the proposed design process and the utility of FEKO simulation software for designing microstrip patch antennas at 3.5 GHz frequency.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123415920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative Study of Single MAC FIR Filter Architectures with Different Multiplication Techniques 采用不同乘法技术的单MAC FIR滤波器结构的比较研究

2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2023-04-05 DOI: 10.1109/ICEEICT56924.2023.10157620

D. Vaithiyanathan, Britto Pari James, K. Mariammal

{"title":"Comparative Study of Single MAC FIR Filter Architectures with Different Multiplication Techniques","authors":"D. Vaithiyanathan, Britto Pari James, K. Mariammal","doi":"10.1109/ICEEICT56924.2023.10157620","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10157620","url":null,"abstract":"Emerging technologies in VLSI signal processing systems demand FIR filters' optimal design to support a wide range of applications. This study presents the architectures for single-channel and multichannel FIR filters employing the Time-division multiplexing (TDM) scheme. The studied architecture is associated with one multiplication and addition unit to handle a wide range of channels and filter taps to have efficient resource utilization. Further accumulator-based Radix-4 multiplier, shift and add multiplication, and parallel pipelined multiplication operations involved in the architectures effectively utilize the resources to a considerable extent. The studied 16-tap multiple channel FIR filter design is simulated using Verilog Hardware Description Language (HDL) and synthesis is carried out using Xilinx Vertex Field Programmable Gate Array (FPGA). In addition, single multiply-accumulate (MAC) based FIR filter architectures with different multiplication-based approaches are implemented, and the results are reported. The analysis and synthesis results conclude that the studied 16 taps single MAC FIR structure offers area (slices) optimization of about 89.6% when examining with the conventional Parallel MAC FIR filter structure. Similarly, the 16-tap single MAC multichannel structure offers area (slices) minimization of about 90.01 % over the corresponding parallel MAC multichannel implementation. Further, the single MAC structure with a single-channel employing OPC (Output Product Coding) scheme offers 95% area reduction and 86% speed increment when compared to the parallel MAC structure with single-channel implementation. Also, the single MAC multichannel design with the OPC scheme offers 19.84% SDP (slice delay product) optimization when compared to the other studied architecture.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125356412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Medical Image Denoising Using BAT Optimization Algorithm 基于BAT优化算法的医学图像去噪

2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2023-04-05 DOI: 10.1109/ICEEICT56924.2023.10157169

K. Sankaran, M. Pradeepa, C. Chandra

{"title":"Medical Image Denoising Using BAT Optimization Algorithm","authors":"K. Sankaran, M. Pradeepa, C. Chandra","doi":"10.1109/ICEEICT56924.2023.10157169","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10157169","url":null,"abstract":"Denoising is critical in medical imaging for the study of pictures, the diagnosis and treatment of illness. Image denoising approaches based on optimization are now effective, however the methods are constrained by the need for a large training set size (i.e., not successful enough for small data size). Medical picture denoising may be accomplished using the discrete wavelet transform (DWT) and a coefficient thresholding-based BAT method (CTB BAT). Denoising images by removing a residual from a noisy image yields denoised images, while most other image denoising methods start with latent clean images and work their way up to learning noise from the noisy images. Additionally, the wavelet transform is incorporated with CTB_ BAT to increase model learning accuracy and training time. Denoising strategies are compared to our model's performance in terms of peak signal-to-noise ratio and structural similarity in order to determine how well it performs compared to other medical picture denoising approaches. Our methodology outperforms other approaches in experiments, as shown by the findings.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125340284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Center Fitting Shadowing Property for Partial Hyperbolic Diffeomorphisms 部分双曲微分同态的中心拟合阴影性质

2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2023-04-05 DOI: 10.1109/ICEEICT56924.2023.10157665

D. M. Al-Ftlawy, Iftichar M. T. Al-Shara’a

引用次数: 0

Post-Quantum Lightweight Encryption Algorithm for Internet of Things Devices 物联网设备的后量子轻量级加密算法

2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2023-04-05 DOI: 10.1109/ICEEICT56924.2023.10157055

A. Dwivedi, Ratish Agarwal, P. Shukla

{"title":"Post-Quantum Lightweight Encryption Algorithm for Internet of Things Devices","authors":"A. Dwivedi, Ratish Agarwal, P. Shukla","doi":"10.1109/ICEEICT56924.2023.10157055","DOIUrl":"https://doi.org/10.1109/ICEEICT56924.2023.10157055","url":null,"abstract":"The increasing use of Internet of Things (IoT) devices in various applications has led to a growing concern about their security. Many IoT devices have limited resources such as processing power, memory, and energy, which makes them vulnerable to attacks. Encryption is a fundamental security mechanism that can be used to protect data in transit and at rest. However, traditional encryption algorithms are often too complex and resource-intensive for IoT devices. In this paper, we propose a lightweight encryption algorithm for IoT devices that is designed to provide a balance between security and resource efficiency. The Sym-BRLE (Binary Ring-Learning encryption) algorithm, based on the binary ring-learning with an error's encryption algorithm, has been proposed to improve random number selection and polynomial multiplication calculations to meet IoT communication requirements. In addition, the algorithm adds encryption security measures to achieve high security and efficiency for lightweight IoT devices. The Sym-BRLE algorithm has high communication efficiency and a small key size, and it can reduce total encryption time by 30% to 40% compared to other BRLE-based encryption algorithms. In addition, security analysis shows that Sym- BRLE can resist grid attacks, timing attacks, simple energy, and differential energy analyses.","PeriodicalId":345324,"journal":{"name":"2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128825465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of SAR ImagesDe-speckling using a Bilateral filter and Feed Forward Neural Networks SAR图像分析利用双边滤波器和前馈神经网络去斑

2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) Pub Date : 2023-04-05 DOI: 10.1109/ICEEICT56924.2023.10156987

M. Kalaiyarasi, Swaminathan Saravanan, Bharath Kumar Narukullapati, I. Kasireddy, D. S. Naga Malleswara Rao, D. Nagineni Venkata Sireesha

引用次数: 0