2022 24th International Multitopic Conference (INMIC)最新文献_第2页

Vehicle Detection and Tracking from UAV Imagery via Cascade Classifier 基于级联分类器的无人机图像车辆检测与跟踪

2022 24th International Multitopic Conference (INMIC) Pub Date : 2022-10-21 DOI: 10.1109/INMIC56986.2022.9972959

Shuja Ali, Muhammad Hanzla, A. Rafique

引用次数: 0

Unveiling the Potential of Vision Transformer Architecture for Person Re-identification 揭示视觉转换架构对人再识别的潜力

2022 24th International Multitopic Conference (INMIC) Pub Date : 2022-10-21 DOI: 10.1109/INMIC56986.2022.9972908

N. Perwaiz, M. Shahzad, M. Fraz

引用次数: 1

Hybrid deep learning based POS tagger for Roman Urdu 基于混合深度学习的罗马乌尔都语POS标注器

2022 24th International Multitopic Conference (INMIC) Pub Date : 2022-10-21 DOI: 10.1109/INMIC56986.2022.9972913

Alishba Laeeq, Masham Zahid, Abdulwadood Waseem, Muhammad Umair Arshad

{"title":"Hybrid deep learning based POS tagger for Roman Urdu","authors":"Alishba Laeeq, Masham Zahid, Abdulwadood Waseem, Muhammad Umair Arshad","doi":"10.1109/INMIC56986.2022.9972913","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972913","url":null,"abstract":"Parts-of-Speech (POS) tagging is a highly encouraged research topic in the field of Natural Language Processing. POS entails numerous practical applications such as text indexing, information retrieval, corpus tagging for research, and linguistic work. This paper outlines multiple methods for part-of-speech tagging in Roman Urdu. Sufficient work and relevant required corpora are not available for Roman Urdu. We have identified that there are several parts-of-speech classes in the Urdu Language, with limited access to a well-annotated corpus. A manually verified corpus has been used to evaluate and report multiple methods for the said task. Our experiments deal with twenty-three unique parts-of-speech classes based on the contextual requirements of the Urdu Language. Our experiments include several methods built upon artificial neural networks, based on approaches such as multi-layered neural networks, feedback recurrent networks, and self-attention models. The corpus we used is not domain specific and covers several topics of Pakistani interest. Our experiments varied to a certain degree in the success demonstrated and outperformed numerous baseline models of machine learning and deep learning.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134457441","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

Enhancing NDVI Calculation of Low-Resolution Imagery using ESRGANs 利用esrgan增强低分辨率图像NDVI计算

2022 24th International Multitopic Conference (INMIC) Pub Date : 2022-10-21 DOI: 10.1109/INMIC56986.2022.9972928

Muhammad Mahad Khaliq, R. Mumtaz

{"title":"Enhancing NDVI Calculation of Low-Resolution Imagery using ESRGANs","authors":"Muhammad Mahad Khaliq, R. Mumtaz","doi":"10.1109/INMIC56986.2022.9972928","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972928","url":null,"abstract":"Normalized Difference Vegetation Index (NDVI) has been one of the key scales for monitoring multiple plant parameters, but satellite imagery is never up to date, which makes it difficult to get readings for the recent situation of field crops. Doing so with Unmanned Aerial System, drone, in this case, is an intricate task, but with its advantages which include timely and effective measurements with the least errors to be fixed in post-processing of data. Before this, NDVI has been calculated using an Unmanned Aerial System, but the problem of the low resolution of the imagery always lingers. With the recent advancement of generated adversarial networks, the up-scaling of images has been made possible, which, if done with the right model, rules out the need for upgrading the camera hardware that is never cost-effective. We have come up with the solution of calculating the vegetation index of field crops by implementing Enhanced Super-Resolution Generated Adversarial Networks with drone imagery to calculate the vegetation index of crop fields. A simple near-infrared spectrum camera is usually not capable of producing a higher resolution image, by implementing the aforementioned generated adversarial network, we have been able to calculate vegetation index for a comparably much higher resolution image without upgrading with sophisticated hardware. We were able to perform the calculations for more pixels (12952) against the same area yielded an output value of 0.829 as compared to 0.828 in the case of low-resolution imagery (546416 pixels). The averaged values for red and near-infrared pixels showed changes from 32.337 to 30.264 for red, and from 189.168 to 182.1656 for near-infrared pixels. The results produced with this technique are different from those generated using original images which account for a new gateway in the calculation of the NDVI.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116159942","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: 1

Saraiki Language Word Prediction And Spell Correction Framework 萨拉基语单词预测和拼写纠正框架

2022 24th International Multitopic Conference (INMIC) Pub Date : 2022-10-21 DOI: 10.1109/INMIC56986.2022.9972938

Muhammad Farjad Ali Raza, M. Naeem

{"title":"Saraiki Language Word Prediction And Spell Correction Framework","authors":"Muhammad Farjad Ali Raza, M. Naeem","doi":"10.1109/INMIC56986.2022.9972938","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972938","url":null,"abstract":"Word prediction, spelling error correction and finding similarity between words are very useful features in any language. The Saraiki is one of the popular languages spoken in Pakistan. To the best of our knowledge, very little work has been done in the literature for word prediction, spell correction and finding similar words for the Saraiki language. In this paper we address these issues by presenting a novel approach for word prediction, finding similar words, and spell correction in the Saraiki language. To achieve this, we used CBOW and Skip-Gram for the vectorization of the Saraiki language. From our results, we achieved word prediction accuracy of 24 % in case of word2vec while 29 % in case of the fastText. In case of word similarity, we achieved similarity score equal to 0.35, and 0.39 for word2vec CBOW and word2vec Skip-Gram respectively and similarity score of 0.35 and 0.41 for the fastText CBOW and the fastText Skip-Gram respectively. Our spell correction results show that as we increase wrong characters in words, the accuracy gets decreased. For sentence-level word prediction, we achieved accuracy of 63% in case of RoBERTa and 58% for distilled respectively.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126430552","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: 1

Real-Time Detection of Knives and Firearms using Deep Learning 使用深度学习的刀具和枪支实时检测

2022 24th International Multitopic Conference (INMIC) Pub Date : 2022-10-21 DOI: 10.1109/INMIC56986.2022.9972915

Abdul Rehman, L. Fahad

引用次数: 0

Lexical Normalization of Roman Urdu 罗马乌尔都语的词汇规范化

2022 24th International Multitopic Conference (INMIC) Pub Date : 2022-10-21 DOI: 10.1109/INMIC56986.2022.9972968

Mamoona Tasadduq

引用次数: 0

Subject Wise Motor Imagery Classification from EEG Data Using Transfer Learning 基于迁移学习的脑电数据运动意象分类

2022 24th International Multitopic Conference (INMIC) Pub Date : 2022-10-21 DOI: 10.1109/INMIC56986.2022.9972989

Afaq Ahmad Khan, A. Hassan, Muhammad Talha Jahangir

{"title":"Subject Wise Motor Imagery Classification from EEG Data Using Transfer Learning","authors":"Afaq Ahmad Khan, A. Hassan, Muhammad Talha Jahangir","doi":"10.1109/INMIC56986.2022.9972989","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972989","url":null,"abstract":"Machine learning (ML) has no doubt virtually helped in nearly all fields of life, including medical sciences. ML models are now being trained, tested and developed with the help of information gained from Electroencephalogram (EEG) Signals. Neural Networks (NN) are being used specifically in this regard to exploit their image classification ability. A special class of NN called Transfer Learning (TL), is used to enhance the capability of NNs. In this paper, EEG signals are extracted and used to classify Left or Right Motor Images of the brain using Inception V3 and VGG 16 models. We try to enhance the accuracy of these TL Models by exploiting a different methodology as compared to other available statistical methods available in the research community. For the said purpose, a dataset from Brain-Computer Interface (BCI) Competition IV 2b was used. EEG signals are extracted and transformed into Short Time Fourier Transform (STFT) images. These STFT images are labeled with either Left or Right Motor Imagery (MI) Class. The transfer learning models are trained using these STFT images and results are also compared with a state-of-the art research, implementing Capsule Networks.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129951214","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

EPP-LFU: An Efficient Producer Popularity-based LFU Policy for the Applications of Named-Data Network EPP-LFU:命名数据网络应用中一种高效的基于生产者人气的LFU策略

2022 24th International Multitopic Conference (INMIC) Pub Date : 2022-10-21 DOI: 10.1109/INMIC56986.2022.9972919

Muhammad Burhan, Ahmad Arsalan, R. A. Rehman

引用次数: 0

Validation of Small Signal Model of an LLC Resonant Converter Based HVDC Modulator 基于LLC谐振变换器的高压直流调制器小信号模型验证

2022 24th International Multitopic Conference (INMIC) Pub Date : 2022-10-21 DOI: 10.1109/INMIC56986.2022.9972975

Noman Khan, Sohail Ahmed Khan, Muhammad Osama Afridi, Tanveer Abbas

{"title":"Validation of Small Signal Model of an LLC Resonant Converter Based HVDC Modulator","authors":"Noman Khan, Sohail Ahmed Khan, Muhammad Osama Afridi, Tanveer Abbas","doi":"10.1109/INMIC56986.2022.9972975","DOIUrl":"https://doi.org/10.1109/INMIC56986.2022.9972975","url":null,"abstract":"Resonant converter based power supplies are widely deployed in many industrial applications for their high efficiency and high power density. For control and regulation of the output voltage in resonant converters, frequency modulation (FM) is an intuitive and preferred method. However, the control-input-to-output transfer function is non-linear function of the operating frequency. The mean operating frequency determines the DC gain and bandwidth of the system for small signal perturbations. In this regard, third order non-linear model for LLC resonant converters is proposed in the literature. At a fixed operating point (i.e. a fixed output voltage corresponding to a particular operating frequency), the model can be treated as fairly linear for small perturbations. It is observed that the DC gain and bandwidth at different operating points is quite different, so deciding an appropriate operating point for a particular application is an important design choice. This research considers the small signal model of an 8.8kV/2A LLC resonant converter designed with resonant frequency ($f_{r}$) of 22.7kHz and quality factor (Q)=4 for an industrial magnetron as a specific load. The model is validated through simulations and hardware experiments using the LLC resonant converter. For our specific system DC gain at $f_{r}$ is - 65dB and bandwidth is 310Hz. DC gain and bandwidth of the system at different operating frequencies give an insight to decide the appropriate operating point. Hence, this work offers a strong foundation for a controller design for the LLC resonant converter under consideration.","PeriodicalId":404424,"journal":{"name":"2022 24th International Multitopic Conference (INMIC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128330804","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