2021 12th International Conference on Information and Communication Systems (ICICS)最新文献

{"title":"A Mobile-Based Novice Agriculturalist Plant Care Support System: Classifying Plant Diseases using Deep Learning","authors":"Mariam Reda, Rawan Suwwan, Seba Alkafri, Yara Rashed, T. Shanableh","doi":"10.1109/ICICS52457.2021.9464561","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464561","url":null,"abstract":"For novice gardeners and small-scale farmers, identifying the exact diseases that plague their plants and determining the best treatments for the species affected can be a difficult task without the correct expert knowledge, yet is one that is crucial to sustain successful plant growth. Our project aims to assist gardeners who do not have the required expert knowledge about the characteristics of species-and-disease combinations to avoid the major pitfalls of misidentifying or mistreating diseases by developing a plant care support system containing a high-accuracy, multi-label classification model to classify the [species-disease] combination of a plant non-invasively from an image of the plant’s leaf. The project also aims to provide further agricultural guidance to gardeners by outlining the appropriate plant care details for identifying, treating, and preventing the classified [species-disease] combinations, helping gardeners grow their field knowledge. Our work consists of a brief comparative analysis between lite, mobile-optimized CNN models to attempt to maximize the accuracy and performance of our classification solution, building on pre-trained base networks using transfer learning. We investigate the effects of varying the retrained portions of the base networks, the effects of using different CNN architectures, and the effects of varying the network hyperparameters on the models’ performances. With these objectives, 32 model variations were developed and evaluated using various standard metrics including accuracy, F1-score, and confusion matrices. The best performing model was found to be an EfficientNetB0 model using a fully retrained base network with optimized hyperparameters, and was then integrated into a system composed of a frontend mobile application and backend centralized cloud database. Beyond the classification and plant care support functionalities, the project also aims to generate new spatiotemporal analytics about the common global species-disease trends by region and season using the collective users’ classification results, making these analytics available to all system users and contributing to the efforts of better understanding global agricultural trends.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"02 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127161668","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}

{"title":"When Proof-of-Work (PoW) based blockchain meets VANET environments","authors":"El-hacen Diallo, O. Dib, N. R. Zema, Khaldoun Al Agha","doi":"10.1109/ICICS52457.2021.9464609","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464609","url":null,"abstract":"Smart vehicles, through dedicated sensors, can gather and announce information about road traffic events. This information can be exploited to improve the transportation system. However, with the ubiquity of cyber-attacks, it is challenging to safely store and share event messages collected through Vehicular Ad hoc NETworks (VANETs). Therefore, in this work, we leverage blockchain technology capability to establish a decentralized, transparent and robust database to build, in turn, a robust and secure traffic event management protocol. We adapt the Proof of Work (PoW) consensus mechanism to this context while testing network configurations via NS-3. The performance of the blockchain is evaluated and its reliability in the presence of attackers (malicious vehicles) is examined and discussed. The results highlight a trade-off between the events’ trustworthiness and the blockchain’s reliability and security.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129612651","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}

{"title":"Automatic Hyperparameter Optimization for Arbitrary Neural Networks in Serverless AWS Cloud","authors":"Alex Kaplunovich, Y. Yesha","doi":"10.1109/ICICS52457.2021.9464618","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464618","url":null,"abstract":"Deep Neural Networks are the most efficient method to solve many challenging problems. The importance of the subject can be demonstrated by the fact that the 2019 Turing Award was given to the godfathers of AI (and Neural Networks) Yoshua Bengio, Geoffrey Hinton, and Yann LeCun. In spite of the numerous advancements in the field, most of the models are being tuned manually. Accurate models became especially important during the novel coronavirus pandemic.Many day-to-day decisions depend on the model predictions affecting billions of people. We implemented a flexible automatic real-time hyperparameter tuning approach for arbitrary DNN models written in Python and Keras without manual steps. All of the existing tuning libraries require manual steps (like hyperopt, Scikit-Optimize or SageMaker). We provide an innovative methodology to automate hyper-parameter tuning for an arbitrary Neural Network model source code, utilizing Serverless Cloud and implementing revolutionary microservices, security, interoperability and orchestration. Our methodology can be used in numerous applications, including Information and Communication Systems.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128127643","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}

{"title":"COVID-19 Diagnosis in Chest X-ray Images by Combining Pre-trained CNN Models with Flat and Hierarchical Classification Approaches","authors":"M. Daoud, Yara Alrahahleh, Samir Abdel-Rahman, B. Alsaify, R. Alazrai","doi":"10.1109/ICICS52457.2021.9464532","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464532","url":null,"abstract":"Novel coronavirus disease 2019 (COVID-19) is highly contagious and can lead to serious medical complications. Early detection of COVID-19 is important to control the spread of the disease and reduce the associated mortality rates. The reverse transcription polymerase chain reaction (RT-PCR) is commonly used for COVID-19 diagnosis. However, the RT-PCR is time consuming, requires special materials, and might have limited detection sensitivity in mild cases. One of the promising complementary modalities to improve the detection and tracking of COVID-19 is X-ray imaging of the chest, but the task of interpreting chest X-ray images is challenging. Convolutional neural networks (CNNs) provide an effective computational tool for classifying chest X-ray images with the goal of achieving accurate COVID-19 diagnosis. This study investigates the application of two pre-trained CNN models, namely AlexNet and ResNet-50, using transfer learning to classify chest X-ray images as normal, pneumonia (non-COVID-19 pneumonia), and COVID-19. The transfer learning process was applied based on two classification approaches, which are the flat classification approach and the hierarchical classification approach. The performance of the proposed CNN-based classification schemes has been evaluated using a dataset that includes 8,703 chest X-ray images. The results indicate that the pre-trained CNN models combined with the hierarchical classification approach achieved effective classification of chest X-ray images. In particular, the pre-trained AlexNet model that is combined with the hierarchical classification approach obtained macro-averaged classification specificity, sensitivity, and F1 score of 98.3%, 89.1%, and 91.4%, respectively. Furthermore, the pre-trained ResNet-50 model that is combined with the hierarchical classification approach achieved macro-averaged specificity, sensitivity, and F1 score of 97.4%, 95.2%, and 94.9%, respectively.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124503502","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}

{"title":"Adaptive Higher-Order Sliding Mode Control Based Fuzzy Logic T-S for Lateral Dynamics of Autonomous Vehicles","authors":"Rachid Alika, E. Mellouli, E. Tissir","doi":"10.1109/ICICS52457.2021.9464623","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464623","url":null,"abstract":"In this paper, we have used the super-twisting sliding mode control to control the lateral dynamics of the autonomous vehicle. We approximate the equivalent command used to control the autonomous vehicle by fuzzy logic type Takagi-Sugeno and also we optimize the parameters of this controller using the optimization of the particle swarm (PSO) to improve the performance of this control. And more specifically we used fuzzy logic by combining the adaptive control mechanism based on Lyapunov stability theory to approximate the equivalent control. In these studies we propose a control method without knowledge of the dynamics of the automobile vehicle system based on the use fuzzy logic technique type Takagi-Sugeno (T-S). The goal of this controller is that the autonomous vehicle becomes able to follow the reference trajectory and reduce the lateral displacement error as much as possible. The command is the steering angle, the lateral displacement is the output of this system which depends on the variation of the yaw angle. The simulation results obtained by our proposed controller are better.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128034256","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}

{"title":"An Optimized KDD Process for Collecting and Processing Ingested and Streaming Healthcare Data","authors":"Argyro Mavrogiorgou, Athanasios Kiourtis, George Manias, D. Kyriazis","doi":"10.1109/ICICS52457.2021.9464551","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464551","url":null,"abstract":"Nowadays organizations are surrounded with enormous amounts of data, losing all the important information that resides in it. Knowledge Discovery in Databases (KDD) can aid organizations to transform this data into valuable information, by extracting complex patterns and relationships from it. To achieve that, various KDD techniques and tools have been proposed, resulting into impressive outcomes in various domains, especially in healthcare. Due to the huge amount of data available within the healthcare systems, data mining is extremely important for the healthcare sector. However, what is of major importance as well, is the way through which the data is collected, preprocessed and integrated with each other, considering its heterogeneous and diverse nature and format. To address all these challenges, this paper proposes a generalized KDD approach, which in essence constitutes a supplement of all the existing approaches that study and analyse the data mining part of the KDD process. This approach primarily concentrates on the phases of the selection, the preprocessing, as well as the transformation of the collected healthcare data, which are considered to be of great importance for its successful mining, analysis, and interpretation. The prototype of the proposed approach provides an example of the developed mechanism, explaining in deep detail its phases, verifying its possible wide applicability and adoption in various healthcare scenarios.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"26-27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132361740","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}

{"title":"Attitudes Evaluation Toward COVID-19 Pandemic: An Application of Twitter Sentiment Analysis and Latent Dirichlet Allocation","authors":"Saeed Shurrab, Yazan Shannak, Abdulkarem Almshnanah, Huthaifa Khazaleh, Hassan M. Najadat","doi":"10.1109/ICICS52457.2021.9464558","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464558","url":null,"abstract":"USA is among the countries that have been considerably affected by the COVID-19 to contain the largest proportion of cases globally. This research aims at investigating the American Community opinions polarity toward the outbreak of the virus in the US throughout twitter social media platform. Further, a topic modeling approach was employed to gain insights about the most discussed topic by the US community during pandemic. A total number of 1,385,469 tweets were collected for the purpose of the study over the period of early February to late April. In addition, unsupervised approaches were employed in the analysis including VADER lexicon for sentiment analysis and Latent Dirichlet Allocation (LDA) for topic modelling. The main findings of the research showed that the largest share of the collected tweets is of positive sentiment followed by negative and neutral. Further, temporal sentiment analysis on monthly basis in comparison with COVID-19 cases showed how the tweets polarity changed over time from early February to late April. In total, the polarity of the tweets was negative before the virus outbreak and positive during the outbreak. In addition, LDA analysis showed that the overall discussed topics tend are oriented toward economy, politics, and the spread of the virus outside the US in February while March and April topics are oriented toward discussing the prevention from the virus as well as spread of the virus inside USA.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133464012","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}

{"title":"Multi-Label Arabic Text Classification Based On Deep Learning","authors":"Batool Alsukhni","doi":"10.1109/ICICS52457.2021.9464538","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464538","url":null,"abstract":"Multi-label text classification is a natural extension of text classification in which each document can be assigned with a possible widespread set of labels. Natural Language Processing (NLP) helps to understand and manipulate text in natural language by using the computer. Arabic Text Classification is challenging recently because the Arabic language is under-resourced although it has many users. The aim of this paper is to build a model to classify Arabic news and help users get and display the most relevant news to their interests. In this paper, we demonstrate the efficiency of using deep learning models in solving Arabic multi-label text classification problem. Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) have been used; we build two models via python. All data has been cleaned to improve the quality of experimental data. The result of test data in LSTM was 82.03 whereas in the MLP model was 80.37, and both models were evaluated using F1 score.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129825200","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}

{"title":"Experience Simple Transformer library in solving Mojaz Multi-Topic Labelling Task","authors":"Mo’ataz A. Ajlouni","doi":"10.1109/ICICS52457.2021.9464602","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464602","url":null,"abstract":"This article describes the code that was used in a multi-topic labeling system, the code starts with loading the train, validation, and test data set then installing the pyarabic and Simple Transformers libraries.\" pyarabic\" allows the system to manipulate Arabic letters. \"Simple Transformers\" is a Natural Language Processing (NLP) library designed to simplify the usage of Transformer models without having to compromise on utility. The model was used from the Simple Transformer library is \"Multi Label Classification Model\" with the model type \"bert\" and the model’s name \"asafaya/bert-base-arabic\". In multi-label text classification, the target for a single article (row) from the training dataset is a list of 10 distinct binary labels. A transformer-based multi-label text classification model typically consists of a transformer model with a classification layer on top of it. The results were impressive compared to the short training time for two epochs, which is five minutes and 27 seconds. The accuracy results were as follows: F1 macro: 0.866, F1 micro: 0.869, competition website on Codalab: 0.8468.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129856293","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}

{"title":"Landmark based Outliers Detection in Pervasive Applications","authors":"Kostas Kolomvatsos, C. Anagnostopoulos","doi":"10.1109/ICICS52457.2021.9464571","DOIUrl":"https://doi.org/10.1109/ICICS52457.2021.9464571","url":null,"abstract":"The combination of the Internet of Things and the Edge Computing gives many opportunities to support innovative applications close to end users. Numerous devices present in both infrastructures can collect data upon which various processing activities can be performed. However, the quality of the outcomes may be jeopardized by the presence of outliers. In this paper, we argue on a novel model for outliers detection by elaborating on a ‘soft’ approach. Our mechanism is built upon the concepts of candidate and confirmed outliers. Any data object that deviates from the population is confirmed as an outlier only after the study of its sequence of magnitude values as new data are incorporated into our decision making model. We adopt the combination of a sliding with a landmark window model when a candidate outlier is detected to expand the sequence of data objects taken into consideration. The proposed model is fast and efficient as exposed by our experimental evaluation while a comparative assessment reveals its pros and cons.","PeriodicalId":421803,"journal":{"name":"2021 12th International Conference on Information and Communication Systems (ICICS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121131109","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}