2023 4th International Conference on Advancements in Computational Sciences (ICACS)最新文献_第3页

An Optimized Algorithm for Human Portrait Image Segmentation Using U-Net 基于U-Net的人体肖像图像分割优化算法

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089739

Abdul Jamil, Mohsin Ashraf, Asif Farooq, Muhammad Bilal Khan, Usman Ahmed, Muhammad Umair

{"title":"An Optimized Algorithm for Human Portrait Image Segmentation Using U-Net","authors":"Abdul Jamil, Mohsin Ashraf, Asif Farooq, Muhammad Bilal Khan, Usman Ahmed, Muhammad Umair","doi":"10.1109/ICACS55311.2023.10089739","DOIUrl":"https://doi.org/10.1109/ICACS55311.2023.10089739","url":null,"abstract":"Segmentation is a technique used in image analysis that involves the division of an image into smaller, more manageable regions corresponding to distinct objects. Image segmentation can be accomplished in a variety of ways from simple hand-specified regions to intelligent auto-detected regions of interest. Regions of interest can be different objects in an image or different color, foreground, and background of an image. Segmentation process is different for each type of application and there is a lack of a universal process that can be applied to all image segmentation tasks. Experts in the field have proposed many Neural Network-based solutions yet unable to achieve significant results segmenting human portraits. To address this issue, this article proposes the use of U-Net model incorporated with alpha matting, for image segmentation of people, separating foreground and background. For experiments, Matting Human Dataset has been used that is publically available on Kaggle. We evaluated the performance of our proposed model and obtained the Jaccard similarity index 0.95 and Dice similarity index 0.72. Empirically, our proposed model takes the advantages of using U-Net model to accomplish reliable results when compared with the other state of the art methods.","PeriodicalId":357522,"journal":{"name":"2023 4th International Conference on Advancements in Computational Sciences (ICACS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122868327","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

Anti-social Behavior Detection using Multi-lingual Model 基于多语言模型的反社会行为检测

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089659

Hafiz Zeeshan Ali, Adnan Rashid Chaudhry

{"title":"Anti-social Behavior Detection using Multi-lingual Model","authors":"Hafiz Zeeshan Ali, Adnan Rashid Chaudhry","doi":"10.1109/ICACS55311.2023.10089659","DOIUrl":"https://doi.org/10.1109/ICACS55311.2023.10089659","url":null,"abstract":"In the current era, social media has emerged as a very useful and reliable means of communication between different people and communities. However, with the leverage of communication platforms and billions of social media users, it became more challenging to stop hateful, abusive, or offensive content spread by extremists that are various aspects of Anti-social Behavior (ASB). Multiple users from several regions use different languages (a mix of native, local and other languages) to express their emotions. Roman Urdu-English and Roman Hindi-English are the two most commonly used languages on social media in the South Asia region. Therefore, the ASB detection with multilingual (multiple languages) model settings represents a wide area of interest for all kinds of social media platforms. Failing to properly address this issue over time on a global scale has already led to morally questionable real-life events, human deaths, and the perpetuation of hate itself. In this paper, we perform a sentimental analysis of the Roman Urdu-English and Roman Hindi-English languages using transformer based mBERT and XLM-R models. Moreover, we process the negatively classified sequences for detection of the ASB.","PeriodicalId":357522,"journal":{"name":"2023 4th International Conference on Advancements in Computational Sciences (ICACS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124512927","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

3D Shape Estimation from RGB Data Using 2.5D Features and Deep Learning 基于2.5D特征和深度学习的RGB数据三维形状估计

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089663

Hamid Ashfaq, Ahmad Jalal

引用次数: 7

Track Coalescence Avoidance in Multi-target Tracking 多目标跟踪中的航迹合并避免

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089762

S. Memon, Wan-Gu Kim, T. Yazdan

引用次数: 0

Detection of Lungs Cancer Through Computed Tomographic Images Using Deep Learning 利用深度学习技术通过计算机断层图像检测肺癌

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089652

Madiha Abid, Shahzad Akbar, S. Abid, Syed Ale Hassan, Sahar Gull

{"title":"Detection of Lungs Cancer Through Computed Tomographic Images Using Deep Learning","authors":"Madiha Abid, Shahzad Akbar, S. Abid, Syed Ale Hassan, Sahar Gull","doi":"10.1109/ICACS55311.2023.10089652","DOIUrl":"https://doi.org/10.1109/ICACS55311.2023.10089652","url":null,"abstract":"Lung cancer has become a particularly lethal disease in the last decade. Lung cancer is the second most common cause of death for women and the primary cause of death for men. Therefore, early detection of lung knobs is one of the most effective ways to treat lung infections. Similarly, computer-aided diagnosis (CAD) of lung knobs has gotten a huge interest over the last decade. As a result of the broad variety of lung knobs and the complications of the entire environment, developing a robust knob detection approach is extremely difficult. A convolutional neural network (CNN) based framework is proposed to detect tumors that are identified as risky or benign in lung disease screening using CT images. Two publicly available datasets LUNA-16 and LIDC are employed to detect lung cancer. The dataset is augmented to maximize the volume of images in it. Also, preprocessing is done on CT images for better noise removal. Additionally, segmentation is performed to specify the infected area. Three pre-trained architectures, DenseNet, AlexNet, and VGG-16, are utilized to classify the cancerous and normal images. The DenseNet classifier achieved 98% classification accuracy, 98.93% sensitivity, and 99% specificity, which exhibits outstanding performance than other classifiers. The efficient results of the proposed framework show better performance than existing state-of-art studies.","PeriodicalId":357522,"journal":{"name":"2023 4th International Conference on Advancements in Computational Sciences (ICACS)","volume":"415 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117300070","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

Open research knowledge graph for structuring scholarly contributions using transformers 使用转换器构建学术贡献的开放研究知识图谱

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089637

Mehboob Ali, Abdullah Malik, Maryam Bashir

{"title":"Open research knowledge graph for structuring scholarly contributions using transformers","authors":"Mehboob Ali, Abdullah Malik, Maryam Bashir","doi":"10.1109/ICACS55311.2023.10089637","DOIUrl":"https://doi.org/10.1109/ICACS55311.2023.10089637","url":null,"abstract":"More research papers are being published now than have ever been at any point in history. It is becoming increasingly difficult for the researchers to keep up with the papers that are being published in even a very narrow domain. This study proposes to build an open research knowledge graph (ORKG) that shows the scholarly contributions of the published papers. The paper makes use of natural language processing techniques and state-of-the-art deep learning models to achieve this task. The system generates a knowledge graph after performing four main steps including sentence classification, phrase extraction, triple formation (and classification) and finally, knowledge graph generation. Different state-of-the-art deep learning models such as RoBERTa have been used for classification and phrase extraction tasks whereas triple formation was performed using different heuristics. Finally, a knowledge graph is generated through which an end-user can identify the scholarly contributions in scholarly article. Experimental results are compared against other systems and show encouraging results.","PeriodicalId":357522,"journal":{"name":"2023 4th International Conference on Advancements in Computational Sciences (ICACS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130237242","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

Multipath Mitigation for Single Frequency Stand-Alone Receivers Using Wavelet Denoising 基于小波去噪的单频独立接收机多径抑制

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089676

Aqsa Zulfiqar, S. Z. Farooq

{"title":"Multipath Mitigation for Single Frequency Stand-Alone Receivers Using Wavelet Denoising","authors":"Aqsa Zulfiqar, S. Z. Farooq","doi":"10.1109/ICACS55311.2023.10089676","DOIUrl":"https://doi.org/10.1109/ICACS55311.2023.10089676","url":null,"abstract":"Multipath error is one of the major error sources impacting high precision positioning in Global Navigation Satellite Systems (GNSS). Particularly, for commonly available single frequency (SF) receivers, an unaccounted multipath error can severely affect positioning accuracy. For multipath mitigation, SF code measurements can be filtered and these filtered pseudoranges are used for positioning. However, filtering multipath is a challenging problem due its stochastic nature. Wavelet signal processing, used for analysis of non-stationary data, can cater for the stochastic nature of multipath. This paper uses wavelet denoising scheme for reducing multipath error in SF code-pseudoranges. The procedure for selecting wavelet filters is outlined. Finally, the positions obtained with wavelet filtered pseudoranges are compared to positions computed with unfiltered pseudoranges. It is seen that wavelet denoising refines the GNSS observation data by reducing multipath and gives smoothed pseudoranges resulting in higher positioning precision. Therefore, an appropriate selection of wavelet filters results in reduction of multipath to a great extent for SF measurements.","PeriodicalId":357522,"journal":{"name":"2023 4th International Conference on Advancements in Computational Sciences (ICACS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127627055","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

Automate Appliances via Gestures Recognition for Elderly Living Assistance 通过手势识别实现家用电器自动化，帮助老年人生活

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089778

Muhammad Muneeb, Hammad Rustam, Ahmad Jalal

{"title":"Automate Appliances via Gestures Recognition for Elderly Living Assistance","authors":"Muhammad Muneeb, Hammad Rustam, Ahmad Jalal","doi":"10.1109/ICACS55311.2023.10089778","DOIUrl":"https://doi.org/10.1109/ICACS55311.2023.10089778","url":null,"abstract":"Smart homes have grown in popularity not only as a luxury but also because of the numerous benefits they provide. In this research, a home automation system is developed for the elders because as the number of elders rises, so does the probability that patients will develop geriatric problems, which necessitates society to address the issue. It is especially beneficial for senior citizens and disabled youngsters. Many research and innovation are conducting on in the field of gestures recognition. In this project, home automation is performed through the use of gestures to control appliances and contradicting the computer vision approaches as an elder person is not capable for ensuring the environment for the computer vision techniques as it requires proper lightning conditions and angle to ensure the parameters. Sensor embedded Hand glove that collects hand motions has been discussed in this study. The wearable device detects and records tilting, rotation, and acceleration of the hand movement using accelerometers and gyroscopes. Our proposed human gestures recognition (HGR) system recognizes nine different hand gestures taken from benchmarked dataset. We used a combination of features extraction algorithms and a random forest classifier to compare our system's performance with other well-known classifiers. We have achieved an accuracy of 94% over the benchmark HGR dataset. Experiments have shown that the proposed approach has the capability to recognize gestures for controlling home appliances and can be used in healthcare, residences, offices, and educational environments.","PeriodicalId":357522,"journal":{"name":"2023 4th International Conference on Advancements in Computational Sciences (ICACS)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132292273","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: 12

Cover ISBN 封面ISBN

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/icacs55311.2023.10089733

引用次数: 0

Design and Analysis of an Improved Deep Ensemble Learning Model for Melanoma Skin Cancer Classification 黑色素瘤皮肤癌分类的改进深度集成学习模型设计与分析

2023 4th International Conference on Advancements in Computational Sciences (ICACS) Pub Date : 2023-02-20 DOI: 10.1109/ICACS55311.2023.10089716

Muhammad Hasnain Javid, Waqas Jadoon, Haris Ali, Muhammad Danish Ali

{"title":"Design and Analysis of an Improved Deep Ensemble Learning Model for Melanoma Skin Cancer Classification","authors":"Muhammad Hasnain Javid, Waqas Jadoon, Haris Ali, Muhammad Danish Ali","doi":"10.1109/ICACS55311.2023.10089716","DOIUrl":"https://doi.org/10.1109/ICACS55311.2023.10089716","url":null,"abstract":"Due to global warming and the ultraviolet rays of the sun, skin diseases are spreading rapidly. If skin diseases are not treated during early stages, they can be dangerous to human life. Melanoma is a deadly type of skin cancer. Dermatologists find it difficult to diagnose melanoma skin cancer because of its complex structure. Significant human lives could be saved if melanoma cancer was diagnosed quickly and accurately. Expert dermatologists diagnose melanoma by examining the lesion's color images. It is very difficult to diagnose melanoma manually, and there is a risk of human error. Therefore, computer vision-based methods that diagnose melanoma disease correctly are offered, and there is little room for error as compared to manual diagnostic methods. In this research, we took images from multiple publicly available ISIC (International Skin Imaging Collaboration) data sets and developed a balance data set that has 10,500 images for training and testing. An ensemble of four convolution neural network (CNN) architectures (ResNet50, EfficientNet B6, InceptionV3, Xception) were utilized and trained on this dataset for classification of melanoma skin cancer. The experimental results of the proposed model show that it correctly classifies melanoma skin cancer. The proposed model gives satisfactory results as compared to other state-of-the-art methods.","PeriodicalId":357522,"journal":{"name":"2023 4th International Conference on Advancements in Computational Sciences (ICACS)","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115484496","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