2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)最新文献_第3页

Applications of Machine Learning in Digital Forensics 机器学习在数字取证中的应用

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441543

S. Qadir, Basirah Noor

引用次数: 5

Deep Learning for Face Detection: Recent Advancements 人脸检测的深度学习:最新进展

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441476

Hafiz Syed Ahmed Qasim, M. Shahzad, M. Fraz

引用次数: 7

A Refined Approach for Classification and Detection of Melanoma Skin Cancer using Deep Neural Network 基于深度神经网络的黑色素瘤皮肤癌分类与检测的改进方法

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441520

Manahil Babar, Roha Tariq Butt, H. Batool, Muhammad Adeel Asghar, Abdul Raffay Majeed, Muhammad Jamil Khan

{"title":"A Refined Approach for Classification and Detection of Melanoma Skin Cancer using Deep Neural Network","authors":"Manahil Babar, Roha Tariq Butt, H. Batool, Muhammad Adeel Asghar, Abdul Raffay Majeed, Muhammad Jamil Khan","doi":"10.1109/ICoDT252288.2021.9441520","DOIUrl":"https://doi.org/10.1109/ICoDT252288.2021.9441520","url":null,"abstract":"Although being a less common form of skin cancer, melanoma is the deadliest of all, accounting for around three-quarters of skin cancer-related deaths. The epidemiological learnings at hand clearly show the relationship between solar UV radiations and skin cancer. For curing it on time, the early-stage identification of melanoma is very necessary. Depending on the clinical aspects of melanoma, appropriate microscopic (dermoscopic) and macroscopic (clinical) analysis are enacted to detect the malignant melanoma. Digital image classification of skin lesions is the basis of efficient skin cancer diagnosis that reduces the time spent and pain received by victims in detecting early melanoma. In this paper, we introduced computer supported strategies for the recognition of melanoma skin cancer utilizing multiple image processing tools. Firstly, a skin lesion image acts as an input to this system and then various image processing and classification agents deduce the presence of melanoma. These analysis techniques test for the melanoma warning signs like border, color, size, and shape for segmentation and feature extraction. This piece of work describes the various approaches of image processing to have improved diagnosis of melanoma skin cancer.","PeriodicalId":207832,"journal":{"name":"2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"78 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121342631","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: 3

Space Air Ground Integrated Network: Coverage for Accident Monitoring 空间地空综合网络:事故监测覆盖

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441529

S. J. H. Pirzada, Tongge Xu, Jianwei Liu

引用次数: 1

Feature Learning Capacity Assessment of Deep Convolutional Generative Adversarial Network for Action Recognition in a Self-Supervised Framework 自监督框架下深度卷积生成对抗网络动作识别的特征学习能力评估

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441535

Samia Azrab, M. H. Mahmood

引用次数: 0

Performance Analysis of Blackhole and Wormhole Attack in MANET Based IoT 基于MANET的物联网中黑洞和虫洞攻击的性能分析

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441515

Muhammad Nasir Siddiqui, K. R. Malik, T. S. Malik

{"title":"Performance Analysis of Blackhole and Wormhole Attack in MANET Based IoT","authors":"Muhammad Nasir Siddiqui, K. R. Malik, T. S. Malik","doi":"10.1109/ICoDT252288.2021.9441515","DOIUrl":"https://doi.org/10.1109/ICoDT252288.2021.9441515","url":null,"abstract":"In Mobile Ad-hoc Network based Internet of things (MANET-IoT), nodes are mobile, infrastructure less, managed and organized by themselves that have important role in many areas such as Mobile Computing, Military Sector, Sensor Networks Commercial Sector, medical etc. One major problem in MANET based IoT is security because nodes are mobile, having not any central administrator and are also not reliable. So, MANET-IoT is more defenseless to denial-of-service attacks for-example Blackhole, Wormhole, Gray-hole etc. To compare the performance of network under different attacks for checking which attack is more affecting the performance of network, we implemented Blackhole and Wormhole attack by modifying AODV routing protocol in NS-3. After preprocessing of data that is obtained by using Flow-monitor module, we calculated performance parameters such as Average Throughput, Average Packet Delivery Ratio, Average End to End Delay, Average Jitter-Sum and compared it with no. of nodes in MANET-IoT network. Throughput and goodput performance of each node in the network is also calculated by using Trace metric module and compared with each node in the network. This approach is also very helpful for further research in MANET-IoT Security.","PeriodicalId":207832,"journal":{"name":"2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121260757","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: 9

NLP Meets Vision for Visual Interpretation - A Retrospective Insight and Future directions NLP满足视觉解释的愿景-回顾洞察和未来方向

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441517

A. Jamshed, M. Fraz

{"title":"NLP Meets Vision for Visual Interpretation - A Retrospective Insight and Future directions","authors":"A. Jamshed, M. Fraz","doi":"10.1109/ICoDT252288.2021.9441517","DOIUrl":"https://doi.org/10.1109/ICoDT252288.2021.9441517","url":null,"abstract":"Recent advances in the field of NLP (Natural Language Processing) and CV (Computer Vision) have sparked a lot of curiosity among researchers to test the limitations of latest Deep learning techniques by employing them in more complex AI tasks. One such kind of task is VQA (Visual Question Answering) which is inherently divided into many layers of complexities. Some questions are simple having obvious answers while some are more complex which need logical reasoning, common sense and factual knowledge. Starting simple and gradually incorporating complexity, is always a good idea in scientific research and development. At first, datasets were simpler consisting of simple question-answer pairs with images depicting simpler concepts and relatively naive VQA models were trained on them. Slowly, with time, the VQA datasets got more complicated and tangled demanding more cognitive capabilities from VQA models. This evolution pushed the VQA models to be more efficient in matching human cognitive abilities, using reasoning based on common sense and factual knowledge. In this survey, we will first discuss some of the famous datasets in the domain of VQA and then we will discuss some of the crucial advancements in the VQA architectures and what is currently being done for integrating common sense and knowledge into these models. Moreover, reasoning is very crucial for truly intelligent systems but representations in deep learning models are inherently very fuzzy and vague. We need models that can transparently generate reasoning about their predictions like old school expert systems which used to work on symbolic knowledge, so the architectures based on the amalgam of deep learning techniques and Symbolic representations would also be a part of our discussion. We will also shed some light on the impact of transformers in the field of deep learning and how these transformer based models are quickly becoming state-of-the-art in almost every deep learning task.","PeriodicalId":207832,"journal":{"name":"2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117307743","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

Sentiment Analysis on COVID Tweets: An Experimental Analysis on the Impact of Count Vectorizer and TF-IDF on Sentiment Predictions using Deep Learning Models COVID推文的情感分析:计数矢量器和TF-IDF对深度学习模型情感预测影响的实验分析

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441508

G. Raza, Zainab Saeed Butt, Seemab Latif, Abdul Wahid

引用次数: 17

Machine Learning for Performance Enhancement in Fronthaul Links for IOT Applications 用于物联网应用前传链路性能增强的机器学习

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441542

M. Hadi, A. Basit

引用次数: 4

Transfer Learning Grammar for Multilingual Surface Realisation 多语言表面实现的迁移学习语法

2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2) Pub Date : 2021-05-20 DOI: 10.1109/ICoDT252288.2021.9441522

Atif Khurshid, Seemab Latif, R. Latif

{"title":"Transfer Learning Grammar for Multilingual Surface Realisation","authors":"Atif Khurshid, Seemab Latif, R. Latif","doi":"10.1109/ICoDT252288.2021.9441522","DOIUrl":"https://doi.org/10.1109/ICoDT252288.2021.9441522","url":null,"abstract":"Deep learning approaches to surface realisation are often held back by the lack of good quality datasets. These datasets require significant human effort to design and are rarely available for low-resource languages. We investigate the possibility of cross-lingual transfer learning of grammatical features in a multilingual text-to-text transformer. We train several mT5-small transformer models to generate grammatically correct sentences by reordering and inflecting words, first using monolingual data in one language and then in another language. We show that language comprehension and task-specific performance of the models benefit from pretraining on other languages with similar grammar rules, while languages with dissimilar grammar appear to disorient the model from its previous training. The results indicate that a model trained on multiple languages may familiarize itself with their common features and, thus, require less data and processing time for language-specific training. However, the experimental models are limited by their entirely text-to-text approach and insufficient computational power. A complete multilingual realisation model will require a more complex transformer variant and longer training on more data.","PeriodicalId":207832,"journal":{"name":"2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126572098","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