2023 9th International Conference on Information Technology Trends (ITT)最新文献

Prediction of Steel Plate Fault Classification Using CART Fuzzy Logic and ANFIS Models 基于CART模糊逻辑和ANFIS模型的钢板故障分类预测

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184259

M. Akpinar, M. F. Adak

引用次数: 0

A Comparative Study of Unauthorized Drone Detection Techniques 非法无人机检测技术的比较研究

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184232

C. Koulouris, Piromalis Dimitrios, Izzat Al-Darraji, Georgios Tsaramirsis, Hatem Tamimi

{"title":"A Comparative Study of Unauthorized Drone Detection Techniques","authors":"C. Koulouris, Piromalis Dimitrios, Izzat Al-Darraji, Georgios Tsaramirsis, Hatem Tamimi","doi":"10.1109/ITT59889.2023.10184232","DOIUrl":"https://doi.org/10.1109/ITT59889.2023.10184232","url":null,"abstract":"The detection of unauthorized drones has emerged as a crucial concern in contemporary times, given the escalating employment of drones for malevolent intentions. Numerous sensor technologies exist for detecting unauthorized drones, each possessing unique benefits and drawbacks. The present study presents an examination of the prominent sensors utilized for the detection of aerial entities and unmanned aerial vehicles (UAVs), and puts forth a practical Guidelines for an unsanctioned Drone Detection mechanism. The array of sensor technologies comprises radar, visible spectrum cameras, infrared cameras, radio frequency detectors, scanning lasers, and human optical detection and identification. The most effective strategy for detecting unauthorized drones may entail integrating various sensor technologies to enhance the comprehensiveness and dependability of drone detection. Through the utilization of sensor technologies, it is feasible to detect and distinguish unapproved unmanned aerial vehicles and implement suitable measures to avert any potential harm or disturbance.","PeriodicalId":223578,"journal":{"name":"2023 9th International Conference on Information Technology Trends (ITT)","volume":"46 2 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":"123520776","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 Enhanced Machine Learning Approach to Identify Noise and Detect Relevant Structures for Predictive Modeling 一种用于预测建模的增强机器学习方法识别噪声和检测相关结构

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184237

M. Uddin

{"title":"An Enhanced Machine Learning Approach to Identify Noise and Detect Relevant Structures for Predictive Modeling","authors":"M. Uddin","doi":"10.1109/ITT59889.2023.10184237","DOIUrl":"https://doi.org/10.1109/ITT59889.2023.10184237","url":null,"abstract":"The era of big data and social networking platforms have provided great repositories of the data for mining useful information for the real-world industry. However, along with this benefit comes the noise in the data. Generally, noise is the data-set that are redundant, false, bad, and/or outliers. Data cleaning, outlier identification, feature engineering, data slicing, etc. are few of many techniques used traditionally. End goal remains ensuring good data (signal) is not lost in bad data (noise) and less processing cost are incurred to extract useful knowledge out of given big data. This paper presents a follow up progress on existing work of the author in relevance of machine learning algorithms, academic and career data predictions and personality computing. All of that have been initially inspired by potential of useful relationships and data points in unstructured data and thus Noise becomes very relevant and may appear Signal in other contexts and predictors in goal. This proposed model is collectively titled as ‘Noise Removal and Structured Data Detection’ based on inherited parallel processing and unique n-Dimensional training approach. Personality features can be quantified into talent traits, matrix indicating the max/min for relevance factors in the academics/career of nD. The engine internals examine and train the algorithm that it minimizes the x,y co-ordinates and maximizes the z co-ordinate. It records and compares the engine internal metrics and reports it back to engine to further optimize the machine learning process until the optimum results are obtained or do not improve any further.","PeriodicalId":223578,"journal":{"name":"2023 9th International Conference on Information Technology Trends (ITT)","volume":"3 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":"130753080","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

PCOS-WaveConvNet: A Wavelet Convolutional Neural Network for Polycystic Ovary Syndrome Detection using Ultrasound images 基于小波卷积神经网络的超声图像多囊卵巢综合征检测

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184271

Shamik Tiwari, P. Maheshwari

{"title":"PCOS-WaveConvNet: A Wavelet Convolutional Neural Network for Polycystic Ovary Syndrome Detection using Ultrasound images","authors":"Shamik Tiwari, P. Maheshwari","doi":"10.1109/ITT59889.2023.10184271","DOIUrl":"https://doi.org/10.1109/ITT59889.2023.10184271","url":null,"abstract":"Women of reproductive age are susceptible to polycystic ovarian syndrome (PCOS), a hormonal condition. Multiple small follicles or cysts on the ovaries are one of the symptoms of PCOS and can be found using ultrasound imaging. Wavelet ConvNets have been applied in various applications, including image classification, object detection, and biomedical signal analysis. A Wavelet ConvNet is a type of deep learning model that applies wavelet transformation to input data before feeding it into a convolutional neural network. The wavelet transform is a mathematical technique that breaks down a signal or image into a series of sub-bands, each representing different frequency components of the original data. In this work, A 2D Discrete Wavelet Transform (2D-DWT) with the Haar wavelet is applied to each image. The resulting sub-bands namely Low-Low (LL), Low-High (LH), High-Low (HL), and High-High (HH) are concatenated to create a 4-channel feature map. Further, this concatenated feature map is fed into the ConvNet for classification. The PCOS-WaveConvNet classifier has attained 99.7% accuracy which is better than a usual ConvNet model.","PeriodicalId":223578,"journal":{"name":"2023 9th International Conference on Information Technology Trends (ITT)","volume":"173 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":"131517851","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

Enhancing IoT Intrusion Detection System Performance with the Diversity Measure as a Novel Drift Detection Method 利用分集度量作为一种新的漂移检测方法提高物联网入侵检测系统的性能

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184268

O. A. Mahdi, Ammar Alazab, S. Bevinakoppa, Nawfal Ali, Ansam Khraisat

{"title":"Enhancing IoT Intrusion Detection System Performance with the Diversity Measure as a Novel Drift Detection Method","authors":"O. A. Mahdi, Ammar Alazab, S. Bevinakoppa, Nawfal Ali, Ansam Khraisat","doi":"10.1109/ITT59889.2023.10184268","DOIUrl":"https://doi.org/10.1109/ITT59889.2023.10184268","url":null,"abstract":"The emergence of the Internet of Things (IoT) has revolutionized various sectors, such as healthcare, intelligent homes, agriculture, transportation, and manufacturing. Nevertheless, the rapid growth of IoT networks has introduced new security challenges, making them susceptible to a variety of attacks. In response, machine learning-driven intrusion detection approaches have been developed, which analyze IoT devices' behavior and communication patterns to detect and counteract suspicious activities. While these approaches exhibit high accuracy and low false alarm rates in static contexts, their performance stability in dynamic, evolving environments is yet to be determined. Model drift, the decline in a machine learning model's predictive capacity over time, is a crucial issue that can considerably affect machine learning-based intrusion detection systems if not identified and addressed promptly. Our work presents an innovative IoT Intrusion Detection System that incorporates the Diversity measure as a drift detection method to address model drift in IoT networks. The proposed concept can detect unknown attacks in IoT networks by employing a cutting-edge drift detection technique.","PeriodicalId":223578,"journal":{"name":"2023 9th International Conference on Information Technology Trends (ITT)","volume":"58 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":"134606606","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 Techniques for Permission-based Malware Detection in Android Applications Android应用中基于权限的恶意软件检测的机器学习技术

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184260

I. Khan, Zahoor Ali Khan, Mir Ahmad, A. Khan, Fida Muahmmad, Azhar Imran, Sheeraz Ahmed, Muhammad Khalid Hamid

引用次数: 0

Comparative Analysis of Machine Learning Methods for Multi-Label Skin Cancer Classification 多标签皮肤癌分类的机器学习方法比较分析

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184240

Muhammad Imad, Z. Khan, Shah Hussain Bangash, Irfan Ullah Khan, Sheeraz Ahmad, A. Ishtiaq

{"title":"Comparative Analysis of Machine Learning Methods for Multi-Label Skin Cancer Classification","authors":"Muhammad Imad, Z. Khan, Shah Hussain Bangash, Irfan Ullah Khan, Sheeraz Ahmad, A. Ishtiaq","doi":"10.1109/ITT59889.2023.10184240","DOIUrl":"https://doi.org/10.1109/ITT59889.2023.10184240","url":null,"abstract":"Skin cancer is one of the most common and dangerous diseases due to a lack of awareness of its signs and methods for prevention. Skin cancer disease can be counted as a fourth burden disease around the world, with the rate of deaths dramatically growing globally. Therefore, early detection at an early stage is necessary to stop the spread of cancer. In this paper, we detect and classify multi-label skin cancer and implement the optimal techniques using machine learning and image processing approaches. However, preprocessing methods assist in removing irrelevant and unnecessary features from the label encoder, and standard features are applied to standardize the range of functionality by scaling the input variance unit. Moreover, various machine learning techniques were applied to check the performance of every classifier on the HAM10000_metadata dataset. The experimental analysis was conducted on the HAM10000_metadata dataset, which consists of seven different types of skin cancer. The results analysis shows that machine learning algorithms such as SVM, DT, and GNB obtained the highest accuracy compared to the other classifiers.","PeriodicalId":223578,"journal":{"name":"2023 9th International Conference on Information Technology Trends (ITT)","volume":"3 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":"122179242","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

Adaptive Methodologies for Tenders as a Thrust for Emerging Startups 自适应投标方法作为新兴创业公司的推动力

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184249

Arnav Tiwari, Aniket Mehrotra, Krishnendu Sukumar, Harsh Khatter, Ajay Kumar Shrivstava, Divya Prakash Shrivastava

引用次数: 0

Artificial Intelligence in Education: An Argument of Chat-GPT Use in Education 人工智能在教育中的应用:对Chat-GPT在教育中的应用的论证

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184267

H. Allam, Juan M. Dempere, Vishwesh Akre, Divya Parakash, Noman Mazher, Jinesh Ahamed

{"title":"Artificial Intelligence in Education: An Argument of Chat-GPT Use in Education","authors":"H. Allam, Juan M. Dempere, Vishwesh Akre, Divya Parakash, Noman Mazher, Jinesh Ahamed","doi":"10.1109/ITT59889.2023.10184267","DOIUrl":"https://doi.org/10.1109/ITT59889.2023.10184267","url":null,"abstract":"Artificial intelligence (AI) is a popular concept for modernizing and automating traditional, time-consuming tasks with smart technology. AI can be applied to a wide range of areas, such as healthcare, finance, law, and education. AI has the potential to revolutionize the way we learn by making education more interactive and engaging. One possible step The way forward in this field is through the use of generative artificial intelligence.technologies like the ChatGPT conversational agent. Although enthusiastic techno-utopian cheerleaders are praising the tool.for answering questions, writing essays, summarizing documents,and generating sophisticated codes, it has some pitfalls that were acknowledged by the creators of OpenAI themselves. In this article, we discuss the application of artificial intelligence in education and put the trendy ChatGPT to the test in an educator-Learner context to see how it performs. We also discuss some of the benefits and drawbacks of ChatGPT and demonstrate how it might be utilized in the classroom. It is essential for educators to understand the implications of this technology and to investigate Strategies to modify the educational environment","PeriodicalId":223578,"journal":{"name":"2023 9th International Conference on Information Technology Trends (ITT)","volume":"22 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":"122911539","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

Exploring Sustainability in London Airbnb Rentals: A Data-Driven Analysis of Sustainability Keywords Using AI Algorithms 探索伦敦Airbnb租赁的可持续性:使用人工智能算法对可持续性关键字的数据驱动分析

2023 9th International Conference on Information Technology Trends (ITT) Pub Date : 2023-05-24 DOI: 10.1109/ITT59889.2023.10184263

Asif Malik, Mohammed Hassouna, Madeleine Togher

引用次数: 1