2022 International Conference on Frontiers of Information Technology (FIT)最新文献_第4页

EHG Signal Analysis for Prediction of Term and Preterm using Variational Mode Decomposition and Artificial Neural Networks 用变分模态分解和人工神经网络分析足月和早产的EHG信号

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00056

Muhammad Umar Khan, Sumair Aziz, Khushbakht Iqtidar, Raul Fernandez Rojas

{"title":"EHG Signal Analysis for Prediction of Term and Preterm using Variational Mode Decomposition and Artificial Neural Networks","authors":"Muhammad Umar Khan, Sumair Aziz, Khushbakht Iqtidar, Raul Fernandez Rojas","doi":"10.1109/FIT57066.2022.00056","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00056","url":null,"abstract":"Preterm deliveries are an important cause of mortality and morbidity in newborns. Accurate and early prediction of a premature delivery can prove helpful in providing proper medication and treatment. Recording of electrical activity known as Electrohysterogram (EHG) from the abdominal surface of pregnant women corresponds to the uterus contractions. A new direction is open using EHG signals for the diagnosis of preterm births. In this research, we present a new method for the accurate classification of preterm and term EHG signals. The proposed method first filters a three-channel EHG signal using bandpass filters. Next, we combined the filtered three-channel EHG into one signal using an accumulation operation. The accumulated EHG signal was post-processed through variational mode decomposition (VMD). VMD algorithm splits the input signal into finite modes using center frequencies known as intrinsic mode functions (IMFs). An energy-based intelligent signal reconstruction approach is designed to combine IMFs having an energy level above the computed threshold. Next, the reconstructed EHG signals were split into continuous windows, and time, frequency, and Hjorth features were extracted. These features were fused to construct a distinct feature representation and were reduced using the ReliefF algorithm. We trained an artificial neural network (ANN) to obtain 98.8 % average accuracy using 10-fold cross-validation.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125662224","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

An Image captioning algorithm based on the Hybrid Deep Learning Technique (CNN+GRU) 一种基于CNN+GRU混合深度学习技术的图像字幕算法

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00032

Rana Adnan Ahmad, Muhammad Azhar, Hina Sattar

{"title":"An Image captioning algorithm based on the Hybrid Deep Learning Technique (CNN+GRU)","authors":"Rana Adnan Ahmad, Muhammad Azhar, Hina Sattar","doi":"10.1109/FIT57066.2022.00032","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00032","url":null,"abstract":"Image captioning by the encoder-decoder framework has shown tremendous advancement in the last decade where CNN is mainly used as encoder and LSTM is used as a decoder. Despite such an impressive achievement in terms of accuracy in simple images, it lacks in terms of time complexity and space complexity efficiency. In addition to this, in case of complex images with a lot of information and objects, the performance of this CNN-LSTM pair downgraded exponentially due to the lack of semantic understanding of the scenes presented in the images. Thus, to take these issues into consideration, we present CNN-GRU encoder decode framework for caption-to-image reconstructor to handle the semantic context into consideration as well as the time complexity. By taking the hidden states of the decoder into consideration, the input image and its similar semantic representations is reconstructed and reconstruction scores from a semantic reconstructor are used in conjunction with likelihood during model training to assess the quality of the generated caption. As a result, the decoder receives improved semantic information, enhancing the caption production process. During model testing, combining the reconstruction score and the log-likelihood is also feasible to choose the most appropriate caption. The suggested model outperforms the state-of-the-art LSTM-A5 model for picture captioning in terms of time complexity and accuracy.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125480187","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

Impact Of Code Smells On Software Fault Prediction At Class Level And Method Level 代码气味对类和方法级软件故障预测的影响

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00066

Um-E Um-E-Safia, T. Khan

{"title":"Impact Of Code Smells On Software Fault Prediction At Class Level And Method Level","authors":"Um-E Um-E-Safia, T. Khan","doi":"10.1109/FIT57066.2022.00066","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00066","url":null,"abstract":"The main aim of software fault prediction is the identification of such classes and methods where faults are expecting at an early stage using some properties of the project. Early-stage prediction of software faults supports software quality assurance activities. Evaluation of code smells for anticipating software faults is basic to ensure its importance in the field of software quality. In this paper, we investigate the impact of code smells on software fault prediction at the class level and method level. Previous studies show the impact of code smells on fault prediction. However, using code smells for class level faults prediction and method level fault prediction need more concern. We use defects4j repository for the creation of datasets used in building software fault prediction model-based. We use pseudo labeling for class level prediction and bagging for method level prediction. We extract code smells from different classes and methods and then used these extracted code smells for fault prediction. We compare our prediction results with actual results and see if our prediction is correct in order to do validation.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130930731","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

A Pothole Warning System using Vehicular Ad-hoc Networks (VANETs) 基于车辆自组织网络(VANETs)的坑坑预警系统

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00036

Pedro Ivo Siqueira Nepomuceno, K. Ullah, K. Braghetto, D. Batista

引用次数: 2

TARIQ: Towards Area Adjustment and Rounding of Intermediate Nodes for Quadrilateration in Blockchain Enabled Underwater Beacon Node Localization 基于区块链的水下信标节点定位中四边形中间节点的面积调整与舍入

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00018

U. Draz, Tariq Ali, M. H. Chaudary, A. Sohail, S. Yasin

{"title":"TARIQ: Towards Area Adjustment and Rounding of Intermediate Nodes for Quadrilateration in Blockchain Enabled Underwater Beacon Node Localization","authors":"U. Draz, Tariq Ali, M. H. Chaudary, A. Sohail, S. Yasin","doi":"10.1109/FIT57066.2022.00018","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00018","url":null,"abstract":"Blockchain Enabled IoT-Underwater Acoustic Sensor Networks (IoT-UASNs), is self-organized heterogeneous wireless network which attract a lot of attention from research community. Moreover, in the fields of marine search and water environment monitoring, blockchain uses several sensors for identify the unknown location of nodes. Therefore, the current research in underwater emphasizes the adoption of blockchain localization algorithm that can calculate and secure the unknown node's location with authenticate accuracy and improve its efficiency and localization error. Therefore, we will induce blockchain in underwater with proposing area adjustment and rounding of nodes. Many mechanisms are present for rounding of localization like bilateration, trilateration's and quadrilaterations. The proposed scheme employs quadrilaterations for localization (as it is extended form of all lateration), that provides the low E2E delay, energy consumption, localization error, transmission loss and achieve high packet delivery ratio. Extensive simulations have been run to demonstrate the proposed scheme's performance superiority over state-of-the-art schemes.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123967547","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

Efficient Distance based Response Time Pruning Cache Policy for NDN 基于有效距离的NDN响应时间剪枝缓存策略

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00038

Ahmad Arsalan, Muhammad Burhan, R. A. Rehman

引用次数: 0

A Methodology of Customized Dataset for Cotton Disease Detection Using Deep Learning Algorithms 基于深度学习算法的棉花病害检测定制数据集方法

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00024

M. Tahir, Ayesha Yaqoob, Haiqa Hamid, R. Latif

{"title":"A Methodology of Customized Dataset for Cotton Disease Detection Using Deep Learning Algorithms","authors":"M. Tahir, Ayesha Yaqoob, Haiqa Hamid, R. Latif","doi":"10.1109/FIT57066.2022.00024","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00024","url":null,"abstract":"Agribusiness occupies the lion’s share of Pakistan’s land. It also supports Pakistan’s financial situation. Approximately 62% of Pakistan’s population lives in rural regions and relies on agriculture for a portion of their income. Pakistan is now the 5th-largest producer of cotton and the 3rd-largest consumer/manufacturer of cotton yarn worldwide. Cotton is grown on 6.0 million acres by 1.3 million of the country’s 5 million farmers, or around 15% of the country’s total cultivated land. Cotton fields are plagued by various illnesses that may have a devastating effect on the quality and quantity of the crop. Detection of these disorders has become more common because of image processing. Pathogens often cause plant diseases like germs, fungi, and microbes that thrive in an unsanitary environment. The farmer suffers a significant setback as a result of this. The main purpose of this research is to get to know the disease in a cotton field. We identify plant/leaf disease using the convolutional neural networks (CN) technique with Pooling, Flatten, Dense, and dropout layers to analyze picture data using TensorFlow and Kera’s support. Our dataset has six classes, including 1965 photos of five sick cotton plant classes and one healthy class. We compared three Kera’s applications to get the algorithm’s best accuracy. The applications we used are Xception, InceptionV3 and InceptionRestNetV2. The Xception model shows us the best accuracy, an average of 90.34%.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126166106","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

Let's Prevent Spectre Attacks in the Docker Containers Too 让我们防止幽灵攻击在Docker容器

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00052

Humdah Shakir Khan, Farooque Hassan Kumbhar, J. Shamsi

引用次数: 0

A Simulation Study on the Downlink Performance of Non-Uniform Dense Cellular Networks 非均匀密集蜂窝网络下行性能的仿真研究

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00046

Muhammad Masood, Muhammad Hanaan, K. Shehzad

{"title":"A Simulation Study on the Downlink Performance of Non-Uniform Dense Cellular Networks","authors":"Muhammad Masood, Muhammad Hanaan, K. Shehzad","doi":"10.1109/FIT57066.2022.00046","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00046","url":null,"abstract":"The spiraling growth of mobile data traffic has fuelled the development of future-generation cellular networks. Datahungry applications have gained massive popularity owing to the usage of high-tech devices. Aggressively reusing the available spectrum has largely been considered a viable solution to this data deluge. Meanwhile, combating the excessively increasing interference in the networks requires employing efficient interference management schemes. The majority of the available literature focuses primarily on the employment of interference mitigating schemes for randomly deployed network infrastructures. However, owing to the heterogeneity in the network, it is important to investigate the utility of such schemes in spatially dependent deployment scenarios. This paper investigates the impact of fractional frequency reuse incorporation on the downlink performance of spatially dependent cellular networks. Simulations are used to thoroughly evaluate the utility of the adopted approach. It is observed that the utility of the adopted scheme significantly improves the network coverage albeit at the cost of decreased spectrum efficiency.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117054213","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

A Reliable Graph-Based Routing Algorithm in Residential Multi-Microgrid Systems 住宅多微网系统中一种可靠的基于图的路由算法

2022 International Conference on Frontiers of Information Technology (FIT) Pub Date : 2022-12-01 DOI: 10.1109/FIT57066.2022.00023

K. Shahzad, Sohail Iqbal

{"title":"A Reliable Graph-Based Routing Algorithm in Residential Multi-Microgrid Systems","authors":"K. Shahzad, Sohail Iqbal","doi":"10.1109/FIT57066.2022.00023","DOIUrl":"https://doi.org/10.1109/FIT57066.2022.00023","url":null,"abstract":"With recent technology breakthroughs, employing a power router as a gateway to attach microgrids with a power system is being increasingly important for regulating bidirectional data and power flow. The energy routing algorithm strategy is one of the most critical factors in assessing the performance of a multi-microgrid system, however, existing power routing algorithms have certain limitations. A crucial limitation is the reliable dispatch of power transmission among the microgrids. None of the existing algorithms carry forward the power loss during the transmission. In this article, we introduce optimizations in an existing algorithm to minimize the overall power losses incurred during the transmission process. Therefore, by minimizing the power losses, we shall be able to meet the amount of power delivered to a microgrid, making our system more reliable. The performance of the proposed algorithm has been validated in different scenarios. To visualize the novelty of the proposed algorithm, its results are compared with existing methods in terms of congestion management, power losses, reliability, accuracy, and handling diverse situations, to name a few. We expect our idea would inspire others to introduce novel optimizations in various processes of multi-microgrid systems.","PeriodicalId":102958,"journal":{"name":"2022 International Conference on Frontiers of Information Technology (FIT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122414812","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