2023 9th International Conference on Information Technology Trends (ITT)最新文献_第4页

Quality Grading of Beef using Deep Convolutional Neural Network 基于深度卷积神经网络的牛肉质量分级

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184241

Aarav Sharma, T. Choudhury, Tanmay Sarkar, Madhusmita Mohanty, Nikunj Bansal, S. Mohanty

引用次数: 0

Combatting Drug Crime in the United Arab Emirates: A Drone-Based System for Monitoring and Tracking Suspects 在阿拉伯联合酋长国打击毒品犯罪:一种基于无人机的监控和跟踪嫌疑人的系统

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184245

Omar Alamir, H. Allam, Muna H. Lootah, Medeya Suwaidi, Sareh Parvaz

{"title":"Combatting Drug Crime in the United Arab Emirates: A Drone-Based System for Monitoring and Tracking Suspects","authors":"Omar Alamir, H. Allam, Muna H. Lootah, Medeya Suwaidi, Sareh Parvaz","doi":"10.1109/ITT59889.2023.10184245","DOIUrl":"https://doi.org/10.1109/ITT59889.2023.10184245","url":null,"abstract":"The United Arab Emirates is facing a significant challenge with drug addiction, trafficking, and promotion within its borders. According to Dajani, 6.1 people per million have died from drug use [1]. To address this issue, the Ministry of Interior is dedicated to ensuring the safety and stability of its citizens and residents through improvements in monitoring and tracking systems, communication networks, and collaborative efforts in the maritime, land, and air domains with its partners. Despite the increasing number of drug-related crimes, the UAE is focusing on implementing advanced technologies to swiftly apprehend suspects and reduce the drug crime index. Drug trafficking and usage in the UAE has been a persistent problem since the 1980s, with a high rate of drug-related deaths recorded by Dubai Police in 2001. Over 3.5 tons of drugs were recovered, and 1,307 individuals were arrested for drug-related offenses such as possession, misuse, and sale. Heroin and Hashish were the most commonly used substances. To minimize drug-related crimes and protect society, this paper proposes a drone-based system.","PeriodicalId":223578,"journal":{"name":"2023 9th International Conference on Information Technology Trends (ITT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129456751","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

Fissura_Net: An Enhanced Crack Detection and Prediction Network Fissura_Net:一个改进的裂纹检测和预测网络

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184265

M. Jawahar, Neeraja R, L. Anbarasi, Benson Raj

{"title":"Fissura_Net: An Enhanced Crack Detection and Prediction Network","authors":"M. Jawahar, Neeraja R, L. Anbarasi, Benson Raj","doi":"10.1109/ITT59889.2023.10184265","DOIUrl":"https://doi.org/10.1109/ITT59889.2023.10184265","url":null,"abstract":"Rapid developments in computer vision, big data and deep learning have contributed to automated fault diagnosis in many domains. Intelligent pavement damage prediction is of great significance in transportation maintenance to ensure safety. As the depths of convolution neural networks increases, loss function’s gradient move towards zero. Furthermore, severity of the crack’s inhomogeneity, intricacy of the background—which includes the low contrast with the nearby pavement and the possibility of shadows with a similar intensity results in ambiguous crack prediction. Hence, the crack detection image dataset is preprocessed using homomorphic filtering. To avoid the complications of vanishing gradient, this paper presents three different deep learning architectures (custom model (CR Net), Res-Net50 and Fisura_Net) based on residual and skip connections with hyper parameters tuned optimally to classify and identify crack and no-crack images. Experimental analysis was carried out with a comparison of contemporary CNN models like ResNet-50, ResNet 152 and previous existing research works on the basis of precision, recall, accuracy, F1 score, loss accuracy, and receiver operating characteristic (ROC) curves and Area Under Curve (AUC) metric. The comparative results ascertain the proposed Fisura_Net model utilizing less computational complexity with highest classification prediction efficiency (99.85%), elevated F1 score (1.0) and AUC score (0.99). Fisura_Net outperformed the other existing models and exhibited promising damage prediction.","PeriodicalId":223578,"journal":{"name":"2023 9th International Conference on Information Technology Trends (ITT)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131292957","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

The Influence of AI-Identified Consumer Sentiment Metrics on Crypto-Asset Prices 人工智能识别的消费者情绪指标对加密资产价格的影响

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184246

Juan M. Dempere, Zakea Il-agure, H. Allam, Lakshmana Kumar Ramasamy, S.K.S.K.S. Alqasemi

引用次数: 0

Sign Language Recognition 手语识别

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184252

Harshita Khubchandani, K. T

{"title":"Sign Language Recognition","authors":"Harshita Khubchandani, K. T","doi":"10.1109/ITT59889.2023.10184252","DOIUrl":"https://doi.org/10.1109/ITT59889.2023.10184252","url":null,"abstract":"Deaf and hard-of-hearing persons communicate with each other and their communities by using sign language. As this is a natural method for people to interact with computers, many academics are researching it in order to make it simpler and more natural without the use of additional gadgets. So, the main objective of gesture recognition research is to develop systems that can recognise certain human gestures and use them, for instance, to communicate information. Quick, extremely accurate hand detection and real-time gesture identification must be possible with vision-based hand gesture interfaces. Learning sign movements is the first step in creating words and sentences for computer-assisted sign language interpretation. Both static and dynamic sign actions are available. Both types of gesture recognition are crucial to human culture, even if static gesture recognition is easier than dynamic gesture recognition. This paper outlines the processes needed to recognise sign language. The method of gathering data, as well as its preprocessing, transformation, feature extraction, categorization, and outcomes, are all examined. For this area of study, there are also some suggested directions for further research. The visual-manual modality used by sign languages is a collection of established languages. When a hand motion is inputted, the system instantly displays the appropriate recognized character on the monitor screen. In the following, research projects that led to a system that uses convolutional neural networks to identify handwriting on the basis of depth pictures the camera collects.","PeriodicalId":223578,"journal":{"name":"2023 9th International Conference on Information Technology Trends (ITT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122369290","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

Machine Learning-based Smart Assistance System for the Visually Impaired 基于机器学习的视障人士智能辅助系统

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184257

Peer Azmat Shah, Hessah Ali Rashed Ali Alhefeiti, Rayan Hussain Mohamed Ali Alrayssi

{"title":"Machine Learning-based Smart Assistance System for the Visually Impaired","authors":"Peer Azmat Shah, Hessah Ali Rashed Ali Alhefeiti, Rayan Hussain Mohamed Ali Alrayssi","doi":"10.1109/ITT59889.2023.10184257","DOIUrl":"https://doi.org/10.1109/ITT59889.2023.10184257","url":null,"abstract":"Individuals with visual impairments face significant challenges in their daily lives, including mobility issues and difficulties recognizing people and differentiating between places. As a result, there is a pressing need for an intelligent device that can assist these individuals and help alleviate some of the difficulties they encounter. The proposed solution aims to address three of the primary challenges faced by visually impaired individuals: recognizing people, familiarizing themselves with indoor spaces, and receiving notifications in case of danger. This research work seeks to assist individuals who are blind in identifying others using a camera-on-glasses that can perform facial recognition and detect and identify faces. Additionally, the system can provide audio feedback through headphones to inform the user of the identity of individuals in their vicinity. Moreover, the system features a unique indoor navigation feature that utilizes radio frequency and machine learning technology to inform the user of their current location within a building. The system can also learn from its environment to detect emergency situations and alert a loved one in case the visually impaired individual is in danger.","PeriodicalId":223578,"journal":{"name":"2023 9th International Conference on Information Technology Trends (ITT)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128880402","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

Decoding the Black Box: A Comprehensive Review of Explainable Artificial Intelligence 解码黑匣子:可解释人工智能的全面回顾

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184238

Ossama H. Embarak

{"title":"Decoding the Black Box: A Comprehensive Review of Explainable Artificial Intelligence","authors":"Ossama H. Embarak","doi":"10.1109/ITT59889.2023.10184238","DOIUrl":"https://doi.org/10.1109/ITT59889.2023.10184238","url":null,"abstract":"This review explores the current state of Explainable Artificial Intelligence (XAI). This study looks at current advances in XAI research, as well as challenges and the future. To accomplish this, the review will explore XAI literature on a wide range of topics, including current techniques, strategies, and applications. The review begins with an overview of XAI, including its definition, history, and motivations. It will then investigate XAI's current situation as well as explainability approaches. This will entail a look at model-based, post-hoc, and interactive explainability, as well as the methods used to get there, such as feature attribution, rule extraction, and counterfactual analysis. The application of XAI in healthcare, finance, and autonomous systems will be examined. The review will look at the ethical and social consequences of XAI, such as bias, accountability, and transparency. Among the challenges that XAI faces include a lack of uniform measurements and evaluation procedures, interpretable data, and domain-specific expertise. The review will include important trends, patterns, and avenues for improvement. The review will also recommend XAI research and development. This review will assist XAI researchers, practitioners, and policymakers in understanding the field's current state, limitations and challenges, and future direction.","PeriodicalId":223578,"journal":{"name":"2023 9th International Conference on Information Technology Trends (ITT)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114865648","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

Missing Data Recovery for Downstream Learning in Low-Cost Environmental Sensor Networks 低成本环境传感器网络下游学习的缺失数据恢复

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184239

Benjamin Agbo, Hussain Al-Aqrabi, Tariq A. A. Alsboui, Muhammad Hussain, Richard Hill

{"title":"Missing Data Recovery for Downstream Learning in Low-Cost Environmental Sensor Networks","authors":"Benjamin Agbo, Hussain Al-Aqrabi, Tariq A. A. Alsboui, Muhammad Hussain, Richard Hill","doi":"10.1109/ITT59889.2023.10184239","DOIUrl":"https://doi.org/10.1109/ITT59889.2023.10184239","url":null,"abstract":"The development of low-cost sensor networks (LCSNs) has facilitated the generation of vast amounts of environmental monitoring data in air quality monitoring stations. State-of-the-art research has focused on developing robust models to detect and mitigate events which may impact societies. Making adequate predictions is important for meeting the sustainability needs of future generations. Therefore, quality data is required to produce accurate results for downstream analytics and visualisations. When dealing with large amounts of data, it is however common to observe numerous amounts of missing data. This is a challenge for data miners because various methods for data analysis only work well on complete datasets. A traditional approach to handling missing data is to discard instances of missing values and only use complete cases for analysis. However, research has shown that this approach is not practical especially when large amounts of data are missing. This led to an increased need to develop strategies for replacing missing values with plausible values through imputation. This study presents an imputation strategy called k−BFMVI for recovering missing values before training downstream regression models for low cost sensor calibration. Experiments simulated missingness from 10% to 40% using MCAR and MAR mechanisms and the performance of the proposed technique was measured against state-of-the-art techniques. Overall, the proposed algorithm recorded the best imputation accuracy as opposed to existing techniques and showed significant improvements on downstream learning.","PeriodicalId":223578,"journal":{"name":"2023 9th International Conference on Information Technology Trends (ITT)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126544772","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

Towards Standardized AI-Ready Data: A Proposed Framework for the UAE 迈向标准化的ai就绪数据:阿联酋的拟议框架

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184264

Shaikha Shaikha Alqasemi, Zakea Il-agure, Juan M. Dempere

{"title":"Towards Standardized AI-Ready Data: A Proposed Framework for the UAE","authors":"Shaikha Shaikha Alqasemi, Zakea Il-agure, Juan M. Dempere","doi":"10.1109/ITT59889.2023.10184264","DOIUrl":"https://doi.org/10.1109/ITT59889.2023.10184264","url":null,"abstract":"Artificial Intelligence (AI) is a vital component of sustainable development, and the United Arab Emirates (UAE) aims to become a global leader in AI. However, to achieve this goal, there is a need to create a data-sharing framework that provides shared open and standardized AI-ready data collected through a consistent data standard. This research paper proposes a framework for creating such a data-sharing infrastructure in the UAE. The framework addresses challenges such as the lack of standardized data, limited access to data, and inadequate infrastructure, and presents solutions for data standardization, data collection, and data sharing. The paper also highlights the importance of investing in AI infrastructure development, enhancing the skills and capabilities of AI experts and professionals, and developing ethical and legal frameworks for responsible and transparent AI usage. The proposed framework can help the UAE leverage the power of AI to achieve sustainable development goals, improve the quality of life for its citizens, and enhance the overall competitiveness of its economy.","PeriodicalId":223578,"journal":{"name":"2023 9th International Conference on Information Technology Trends (ITT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121243017","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

Understanding Online Customer Touchpoints: A Deep Learning Approach to Enhancing Customer Experience in Digital Retail 理解在线客户接触点:一种深度学习方法来增强数字零售中的客户体验

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184269

Zhao Yilin, Amjad Fayoumi, A. Shahgholian

引用次数: 0