2022 16th International Conference on Open Source Systems and Technologies (ICOSST)最新文献

Diagnosis Of Cassava Leaf Diseases and Classification Using Deep Learning Techniques 基于深度学习技术的木薯叶病诊断与分类

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016854

Syed Mursleen Riaz, Muhammad Ahsan, M. Akram

{"title":"Diagnosis Of Cassava Leaf Diseases and Classification Using Deep Learning Techniques","authors":"Syed Mursleen Riaz, Muhammad Ahsan, M. Akram","doi":"10.1109/ICOSST57195.2022.10016854","DOIUrl":"https://doi.org/10.1109/ICOSST57195.2022.10016854","url":null,"abstract":"The plants disease diagnosis is very challenging research in the field of agriculture. Cassava is a second most provider of carbohydrates in Africa. It is a key food for people of Africa in very harsh conditions. According to United Nations (FAO) almost eighty percent farmers of sub Saharan Africa are growing cassava roots, but due to a variety of viral diseases the production of cassava is very low from last two years. With the help of data science, it is possible to diagnose and classify these types of viral diseases. Existing methods of disease detection require farmers to solicit the help of government-funded agricultural experts to visually inspect and diagnose the plants. Moreover, this process is labor-intensive, time taken, costly and impacting the production and supply cycle. As an added challenge, effective solutions for farmers must perform well under significant constraints since African farmers may only have access to mobile-quality cameras with low-bandwidth. The dataset which we use in this research is taken from Kaggle competition 2020. Dataset contains 21397 images of cassava plants which belongs to five different classes i.e., Cassava Bacterial Blight, Cassava Brown Streak Disease, Cassava Green Mottle, Cassava Mosaic Disease and Healthy leaves. In this work we have used augmentation technique to increase the samples for classification and balancing the uneven distribution of data for all classes and used deep learning model efficiennetB3 for identification classification of diseases and got 83.03% overall accuracy on test dataset with more than 90% individual accuracy of each class. We have developed a graphical user interface for using the model in more efficient way with the aim to help the industry for prediction of diseases during its initial stages.","PeriodicalId":238082,"journal":{"name":"2022 16th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116633582","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

Development of Low-Cost Surgical Simulator for Neuroendoscopy using Unity3D and HTC VIVE 利用Unity3D和HTC VIVE开发低成本神经内窥镜手术模拟器

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016865

Aimen Fatima, Faisal Bukhari, Waheed Iqbal

{"title":"Development of Low-Cost Surgical Simulator for Neuroendoscopy using Unity3D and HTC VIVE","authors":"Aimen Fatima, Faisal Bukhari, Waheed Iqbal","doi":"10.1109/ICOSST57195.2022.10016865","DOIUrl":"https://doi.org/10.1109/ICOSST57195.2022.10016865","url":null,"abstract":"Neuroendoscopy is being utilized more frequently to treat brain tumors and hydrocephalus brought on by tumors. Neuroendoscopy is a minimally invasive operation that requires a lot of skills. Complete training is needed in hospitals to perform this procedure in the operating room. Simulators are necessary to train doctors due to the deficiency of time, resources, cost, and potential risks. With advancements in computer technology, it is becoming more common to simulate and plan surgery using a VR-based simulation. We have created and developed a surgical simulator to provide endoscopic training for the ETV procedure. A method to use multimedia capabilities for learning is to create a virtual 3D simulator using a game engine and render it using VR technology. The user-friendly collaborative environment of the Unity3D game engine offers good features. We have demonstrated how a simplex mesh depiction in a game-based simulator may effectively deform a soft body in actuality. We have successfully developed a method employing Position Based Dynamics to simulate the actual deformation of TM the cancerous brain. According to our study, the HTC Vive is a suitable VR technology for ETV training because it offers excellent precision, quick tactile feedback, a fast frame rate, and a wide field of vision. In the future, this practical tool might replace more costly training simulators.","PeriodicalId":238082,"journal":{"name":"2022 16th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114492822","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

Driver Drowsiness Detection with Region-of-Interest Selection Based Spatio-Temporal Deep Convolutional-LSTM 基于兴趣区域选择的时空深度卷积- lstm驾驶员困倦检测

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016825

Muhammad Saif Basit, Usman Ahmad, Jameel Ahmad, Khalid Ijaz, Syed Farooq Ali

{"title":"Driver Drowsiness Detection with Region-of-Interest Selection Based Spatio-Temporal Deep Convolutional-LSTM","authors":"Muhammad Saif Basit, Usman Ahmad, Jameel Ahmad, Khalid Ijaz, Syed Farooq Ali","doi":"10.1109/ICOSST57195.2022.10016825","DOIUrl":"https://doi.org/10.1109/ICOSST57195.2022.10016825","url":null,"abstract":"Driver fatigue and drowsiness instigate road traffic accidents while driving throughout the years. to reduce road traffic injuries and fatality cases, a real-time drowsiness detection system is needed by using artificial intelligence algorithms to detect drivers' tiredness and drowsiness at an early stage. This study proposes an automatic region-of-interest selection based stacked spatio-temporal convolution-long short-term memory (ConvLSTM) drowsiness detection neural network for an in-vehicle surveillance and security system. Haar Cascade classifiers are used to select the region-of-interest on the human face. A ConvLSTM model is implemented to extract spatio-temporal features from the selected region-of-interest and to predict the drowsiness state of the driver. The performance of the proposed model is compared with various pre-trained deep learning models such as CNN, VGG-16, VGG-19, ResNet-50 and MobileNet. The proposed model is trained on the Yawn Eye and MRL benchmarked image datasets. The proposed approach achieves an accuracy of 99.44% on the Yawn Eye dataset and 90.12% on the MRL dataset. The model is further tested and validated using a live feed camera.","PeriodicalId":238082,"journal":{"name":"2022 16th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129344372","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

Automatic identification of Urdu fake news using Logistic Regression Model 基于Logistic回归模型的乌尔都语假新闻自动识别

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016840

Rana Salahuddin, Muhammad Wasim

引用次数: 1

Deepfakes Examiner: An End-to-End Deep Learning Model for Deepfakes Videos Detection Deepfakes考官:用于Deepfakes视频检测的端到端深度学习模型

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016871

Hafsa Ilyas, Aun Irtaza, A. Javed, K. Malik

{"title":"Deepfakes Examiner: An End-to-End Deep Learning Model for Deepfakes Videos Detection","authors":"Hafsa Ilyas, Aun Irtaza, A. Javed, K. Malik","doi":"10.1109/ICOSST57195.2022.10016871","DOIUrl":"https://doi.org/10.1109/ICOSST57195.2022.10016871","url":null,"abstract":"Deepfakes generation approaches have made it possible even for less technical users to generate fake videos using only the source and target images. Thus, the threats associated with deepfake video generation such as impersonating public figures, defamation, and spreading disinformation on media platforms have increased exponentially. The significant improvement in the deepfakes generation techniques necessitates the development of effective deepfakes detection methods to counter disinformation threats. Existing techniques do not provide reliable deepfakes detection particularly when the videos are generated using different deepfakes generation techniques and contain variations in illumination conditions and diverse ethnicities. Therefore, this paper proposes a novel hybrid deep learning framework, InceptionResNet-BiLSTM, that is robust to different ethnicities and varied illumination conditions, and able to detect deepfake videos generated using different techniques. The proposed InceptionResNet-BiLSTM consists of two components: customized InceptionResNetV2 and Bidirectional Long-Short Term Memory (BiLSTM). In our proposed framework, faces extracted from the videos are fed to our customized InceptionResNetV2 for extracting frame-level learnable features. The sequences of features are then used to train a temporally aware BiLSTM to classify between the real and fake video. We evaluated our proposed approach on the diverse, standard, and largescale FaceForensics++ (FF++) dataset containing videos manipulated using different techniques (i.e., DeepFakes, FaceSwap, Face2Face, FaceShifter, and NeuralTextures) and the FakeA VCeleb dataset. Our method achieved an accuracy greater than 90% on DeepFakes, FaceSwap, and Face2Face subsets. Performance and generalizability evaluation highlights the effectiveness of our method for detecting deepfake videos generated through different techniques on diverse FF++ and FakeA VCeleb datasets.","PeriodicalId":238082,"journal":{"name":"2022 16th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127666501","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: 2

Sparse RIS in Multi User MIMO Wireless System 多用户MIMO无线系统中的稀疏RIS

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016816

Muhammad Haroon Aurangzeb, F. Akram, I. Rashid, Attiq Ahmed

引用次数: 0

Simulating ML-Based Intrusion Detection System for Unmanned Aerial Vehicles (UAVs) using COOJA Simulator 利用COOJA模拟器模拟基于ml的无人机入侵检测系统

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016875

Roshaan Tariq Mehmood, Ghufran Ahmed, Shahbaz Siddiqui

{"title":"Simulating ML-Based Intrusion Detection System for Unmanned Aerial Vehicles (UAVs) using COOJA Simulator","authors":"Roshaan Tariq Mehmood, Ghufran Ahmed, Shahbaz Siddiqui","doi":"10.1109/ICOSST57195.2022.10016875","DOIUrl":"https://doi.org/10.1109/ICOSST57195.2022.10016875","url":null,"abstract":"We are living in an era of IoT devices and the rapid increase in the use of drone applications is evidence of that. UAVs or drones are being used in a variety of industries, ranging from military purposes to delivery purposes, they can be seen everywhere. UAVs come under the umbrella of Unmanned Aerial Systems (UAS). With the increased usage of drones, there is an increased number of cyber-attacks on drones as well. Previously, an IDS solution was developed using Random Forest Classifier algorithm and with help of the CIC-IDS2018 dataset for the identification of these emerging threats. This time, the target is to simulate a UAV environment using COOJA Simulator and evaluate the proposed IDS’ performance. IDS controller architecture is also proposed, which contains a parser, a selector, a router, machine learning algorithms, and a performance analyzer. The functionality of this controller is implemented inside the UAV motes. An SDN controller is used to manage the traffic between the UAV motes and generate malicious and benign traffic for IDS detection. The performance analysis module determines the performance of each algorithm. The best accuracy range was provided by Random Forest Classifier between 95%-96%.","PeriodicalId":238082,"journal":{"name":"2022 16th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114581969","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

UBERT22: Unsupervised Pre-training of BERT for Low Resource Urdu Language UBERT22:低资源乌尔都语BERT的无监督预训练

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016821

Bilal Tahir, M. Mehmood

{"title":"UBERT22: Unsupervised Pre-training of BERT for Low Resource Urdu Language","authors":"Bilal Tahir, M. Mehmood","doi":"10.1109/ICOSST57195.2022.10016821","DOIUrl":"https://doi.org/10.1109/ICOSST57195.2022.10016821","url":null,"abstract":"Natural Language Understanding (NLU) tools have enabled the development of sophisticated and powerful Natural Language Processing (NLP) models. However, this progress is limited to English and European languages and low resource languages lack such tools due paucity of resources. In this paper, we develop UBERT22- an unsupervised pre-trained BERT model for Urdu language. For this purpose, first, we develop a dataset of ‘Zakheera’ containing high-quality content of 1.16 million Urdu language news and blog articles posted on top 37 Urdu websites. Next, we pre-process the text and tokenize content in 21.8 million Urdu sentences. Finally, we extract the word-piece vocabulary of 30,000 tokens and pre-train the BERT model for Masked Language Modelling (MLM) and Next Sentence Prediction (NSP) tasks. We compare the performance of UBERT22 with existing multilingual and small-Urdu BERT for various downstream tasks. We notice that UBERT22 outperforms multilingual and small-Urdu BERT for fake news identification, propaganda classification, topic categorization, and sentiment analysis tasks. Overall, UBERT22 achieves 2-19% higher accu-racy compared to baseline results and competitive BERT models. We believe that the public availability of our pre-trained model and upstream dataset will enable the development of state-of-the-art NLP models of Urdu language such as chatbots, question answering systems, sentiment analyzers, virtual assistants, speech recognizers, and machine translators.","PeriodicalId":238082,"journal":{"name":"2022 16th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123283202","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

Flight Delay Prediction Based on Gradient Boosting Ensemble Techniques 基于梯度推进集成技术的飞行延误预测

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016828

Rahemeen Khan, S. Akbar, Tooba Ali Zahed

引用次数: 2

Word-level Language Identification and Localization in Code-Mixed Urdu-English Text 语码混合乌尔都语-英语文本的词级语言识别与定位

2022 16th International Conference on Open Source Systems and Technologies (ICOSST) Pub Date : 2022-12-14 DOI: 10.1109/ICOSST57195.2022.10016848

Eysha Raazia, Amina Bibi, Muhammad Umair Arshad

{"title":"Word-level Language Identification and Localization in Code-Mixed Urdu-English Text","authors":"Eysha Raazia, Amina Bibi, Muhammad Umair Arshad","doi":"10.1109/ICOSST57195.2022.10016848","DOIUrl":"https://doi.org/10.1109/ICOSST57195.2022.10016848","url":null,"abstract":"Language Identification is significant for most Natural Language Processing (NLP) tasks to work precisely. Language Identification is still very challenging because of the range of dialects. The major challenge in Language Identification (LID) task is the lack of availability of tools for understanding the context of multiple languages. We proposed a deep learning neural network Bi-LSTM CNN for word-level classification for Language Identification (LID) and localization of Roman Urdu and English in the code-switch text in this paper. We utilized the dataset of code-switch text having variant spellings of the same Roman Urdu words, generated from different social media platforms as they are a rich source of code-switch languages. We used GoogleNews Word2Vec Vectorizer for word embeddings. The embedding layer is followed by the Bidirectional long-short term memory (Bi-LSTM) layers along with the Convolutional Neural Network (CNN). We experimented with the dataset on different variations of LSTM and CNN to achieve the best possible results. We achieved 90.40% accuracy and a 90.39% F1 score.","PeriodicalId":238082,"journal":{"name":"2022 16th International Conference on Open Source Systems and Technologies (ICOSST)","volume":"24 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130693041","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