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A roadmap to data science: Background, future, and trends

International Journal of Intelligent Information and Database Systems 2021年第3期14卷 277-293页

作者： Khder, Moaiad Ahmad Fujo, Samah Wael Sayfi, Mohammad Adnan Department of Computer Science Applied Science University Bahrain Department of Computing Engineering and Technology Faculty of Applied Sciences University of Sunderland United Kingdom

Data science is a combination of several disciplines that aims to get accurate insights from a bunch of data, develop the technology, and algorithm to solve the complicated problems analytically. Today, data science plays a massive role in our life, and researchers realise how it is essential. Numerous research studies on data science have been published in recent years, but each focus on specific issues, such as data science and its impact on business, manufacturing, academia, and healthcare. This paper will present a roadmap of data science to benefit the readers to know about this field and realise how it is essential in several areas. In addition, it clarifies the mechanism of how data science work and what are the capabilities of the data scientist to be able to work in this field. It also shows the trends and future work of data science. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Big data

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Enhanced Mini-YOLOv7: A AI Approach for Safety Helmet Detection in the Construction Industry

Enhanced Mini-YOLOv7: A AI Approach for Safety Helmet Detect...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Blessie, A. Alice Sankarasubramanian, R.S. John, Joseph Krishnapriya, S. Bhuvanesh, A. Keerthanadevi, R. Government College of Engineering Department of Electronics and Communication Engineering Tirunelveli India Psg Institute of Technology and Applied Research Department of Mathematics Coimbatore India Gregorian Institute of Technology Department of Mechnanical Engineering Kottayam India Sri Eshwar College of Engineering Department of English Coimbatore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India Psn College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India

ISBN: (纸本)9798331535476

Wearing safety helmets is an effective way to keep construction workers safe on the job. Workers often opt to remove their helmets due to discomfort or a lack of security knowledge, leaving them vulnerable to unsaid threats. A worker's risk of injury from falls, both physical and otherwise, increases when they do not wear a safety helmet. Because detecting whether workers are wearing safety helmets is an important aspect of managing safety on construction sites, a fast and accurate detector is required. However, standard manual monitors aren't particularly popular, and attaching sensors to a safety helmet is a laborious operation. With that in mind, this study presents EM-YOLOv7, a DL method for autonomous safety helmet recognition in real-time that has been developed for use on construction sites. A multi-scale non-local attention module (MSNA) is incorporated into the system to improve detection accuracy in complicated situations. This module enhances feature recovery by aggregating semantic context across multiple scales. Further, to improve detection performance and handle mutual occlusion, a loss function named Wise-IoUv3 (WIoUv3) is used. This method adjusts the loss depending on the overlap between the predicted and ground truth bounding boxes. To train and test the model, we will utilize the safety helmet-wearing (SHWD) dataset, which includes a wide variety of construction workers and whether or not they wore helmets. The goal of this technique is to provide a 99% accurate solution for real-time helmet recognition, which would greatly improve safety management and decrease the likelihood of accidents on construction sites. © 2024 IEEE.

关键词： Construction industry

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Prediction of Hypertension Disease Using Machine Learning Techniques: Case Study from Jordan 25

Prediction of Hypertension Disease Using Machine Learning Te...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Samara, Ghassan Katrawi, Anwar Hadi, Wael Al-Safarini, Maram Y. Al-Mamoori, Farah Elrashidi, Ali Alqaraleh, Muhyeeddin Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of It Department of Computer Science Zarqa Jordan Petra University Faculty of It Information Security Department Amman Jordan Zarqa University Faculty of Pharmacy Department of Pharmaceutical Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of It Department of Software Engineering Zarqa Jordan Applied Science Private University Facultty of Information Technology Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Hypertension is one of the most common diseases in Jordan. It is one of the main reasons of death among Jordanian adult citizens. Worldwide, nearly 13% of all deaths are due to Hypertension with nearly 8 million deaths yearly. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ML) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that Random Forest, Random Committee, and MultilayerPerceptron are the best classification models to handle the task of the early prediction of Hypertension in Jordan. © 2024 IEEE.

关键词： Prediction models

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Face Detection in Video Live Stream Using Deep Learning 2

Face Detection in Video Live Stream Using Deep Learning

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Amrita Kumar, Rajiv Joshi, Shivani Raj, Daksh Centre for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Sharda University U.P. Greater Noida India G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Nvidia Ai Learning & Research Center U.P. Greater Noida India G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering U.P. Greater Noida India Galgotias College of Engineering and Technology Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9798350371055

With the increasing popularity of live streaming platforms, there is an imperative need for robust methods for identifying individuals in real-time. The proposed system utilizes Convolutional Neural Networks (CNN) computer vision algorithms to identify faces in live streams and match them against a pre-existing database of known individuals. Additionally, the system employs deep learning and supervised learning techniques along with various types of CNN to achieve robust facial recognition in diverse lighting and environmental conditions. The system's accuracy is evaluated using various performance metrics, including precision, recall, and F1-score. The results demonstrate that the proposed system achieves high accuracy in person identification during real-time live streams. This system holds potential applications in a variety of domains, including security, entertainment, and social media. © 2024 IEEE.

关键词： computer vision Convolutional neural networks (CNN) deep learning F1-score Live streaming precision recall supervised learning

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Comparison of Parallel B Static Traffic Assignment Algorithm Implementation in Java and C++ 28

Comparison of Parallel B Static Traffic Assignment Algorithm...

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28th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2024

作者： Potuzak, Tomas Pernicka, Petr Kolovsky, Frantisek University of West Bohemia Faculty of Applied Sciences Department of Computer Science and Engineering Plzen Czech Republic RoadTwin S.R.O Plzen Czech Republic

ISBN: (纸本)9798331527211

This paper compares sequential and parallel Java and C++ implementations of the B algorithm, a relatively new algorithm for user-equilibrium (UE) (road) traffic assignment (TA). All the versions were implemented in an optimized way while using best practices in both languages. Their performances were thoroughly tested using multiple large real road traffic networks. The tests showed that the C++ version is indeed faster in most cases, but only by 8.54 % on average. © 2024 IEEE.

关键词： C++ (programming language)

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Forecasting land use changes in crop classification and drought using remote sensing

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Journal of Arid Land 2025年第5期17卷 575-589页

作者： Mashael MAASHI Nada ALZABEN Noha NEGM Venkatesan VEERAMANI Sabarunisha Sheik BEGUM Geetha PALANIAPPAN Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh 11451Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Computer Science Applied College at MahayilKing Khalid UniversityAbha 61421Saudi Arabia Department of Civil Engineering University College of EngineeringAnna UniversityAriyalur 621731India Department of Biotechnology P.S.R.Engineering CollegeSivakasi 626140India Department of Electronics and Communication Engineering School of Engineering and TechnologyDhanalakshmi Srinivasan UniversitySamayapuram 621112India

Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural ***,the effects of urbanization on LULC of different crop types are less *** study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region,Mexico,from 1994 to 2024,and predicted the LULC in 2034 using remote sensing data,with the goals of sustainable land management and climate resilience *** increasing urbanization and drought,the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this *** Landsat imagery,we assessed crop attributes through indices such as normalized difference vegetation index(NDVI),normalized difference water index(NDWI),normalized difference moisture index(NDMI),and vegetation condition index(VCI),alongside watershed delineation and spectral *** random forest model was applied to classify LULC,providing insights into both historical and future *** indicated a significant decline in vegetation cover(109.13 km^(2))from 1994 to 2024,accompanied by an increase in built-up land(75.11 km^(2))and bare land(67.13 km^(2)).Projections suggested a further decline in vegetation cover(41.51 km^(2))and continued urban land expansion by *** study found that paddy crops exhibited the highest values,while common bean and maize performed *** analysis revealed that mildly dry areas in 2004 became severely dry in 2024,highlighting the increasing vulnerability of agriculture to climate *** study concludes that sustainable land management,improved water resource practices,and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the *** findings contribute to the understanding of how remote sensing can be effectively used for long-t

关键词： land use and land cover(LULC) crop attributes drought vulnerability machine learning models remote sensing

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The Effect of Class Distribution on Multi-Modal Medical Images Classification in Meta-Learning

The Effect of Class Distribution on Multi-Modal Medical Im...

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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition and Structural and Syntactic Pattern Recognition, S+SSPR 2024

作者： Almugbel, Zainab Computing Department Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Computer Science and Engineering School of Innovation Design and Engineering Mälardalen University Västerås Sweden

ISBN: (纸本)9783031805066

Medical images are available in small datasets with different modalities and for various organs. Although Transfer learning is a promising approach for training models on small datasets, further studies are required on using them with several image modalities and body parts. This paper explores two dominant meta-learning algorithms: a metric-based algorithm, namely Prototypical Network, and an optimization-based algorithm, namely MAML. The algorithms trained on a small multi-modal medical dataset (i.e., Slake), and a general dataset of the same size (i.e., Tiny-imagenet) with different class distribution methods: Random, Logical-based, and Statistical-based class distribution. The aim is to apply diversity among the classes in the meta-training set and similarity between the classes in the meta-training, and meta-testing or meta-validation sets. The results validate the importance of class distribution on the accuracy of the algorithms. MAML is hard to be trained on Tiny-imagenet but it shows good accuracy on Slake in specific cases. In statistical distribution, the Prototypical Network shows high accuracy on the datasets, especially when the similarity between the meta-training and meta-validation sets is considered for Tiny-imagenet. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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A Continual Learning System with Self Domain Shift Adaptation for Fake News Detection 10

A Continual Learning System with Self Domain Shift Adaptatio...

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10th IEEE International Conference on Data science and Advanced Analytics, DSAA 2023

作者： Basterrech, Sebastian Kasprzak, Andrzej Platos, Jan Wozniak, Michal Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby Denmark Wroclaw University of Science and Technology Department of Systems and Computer Networks Wroclaw Poland VŠB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Ostrava Czech Republic

ISBN: (纸本)9798350345032

Detecting fake news is currently one of the critical challenges facing modern societies. The problem is particularly relevant, as disinformation is readily used for political warfare but can also cause significant harm to the health of citizens, such as by promoting false data on the harmfulness of selected therapies. One way to combat disinformation is to treat fake news detection as a machine learning task. This paper presents such an approach, which additionally addresses an important problem related to the non-stationarity characteristics of the fake news. We elaborated a stream data with the simulation of domain shift based on two popular benchmark datasets dedicated to the fake news classification problem (Kaggle Fake News and Constraint@AAAI2021-COVID19 Fake News Detection). The proposed learning system works in a Continual Learning (CL) framework and integrates a self domain shift adaptation in a machine learning scheme. The method was built following state-of-the-art techniques, that includes Word2Vec as a feature extractor and the LSTM model as a classifier. The performance of the approach has been evaluated over the generated data stream. The convenience of our approach is showed in the results, where the accuracy gain with respect to a CL approach without domain adaptation is observed to be significant. © 2023 IEEE.

关键词： Long short-term memory

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The Role of Soft Skills in engineering Education 53

The Role of Soft Skills in Engineering Education

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Fojcik, Marcin Fojcik, Martyna Katarzyna Ziebinski, Adam Pollen, Bjarte Kampen, Anne-Lena Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Førde Norway Volda University Collage Department of Humanities and Education Volda Norway Silesian University of Technology Department of Distributed Systems and Informatics Devices Gliwice Poland Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9798350336429

The rapid but uneven education development worldwide is creating many challenges. Changing conditions and rules in engineering fields such as programming, automation, and electronics are not always followed by changes in education. Employers expect potential candidates to be current with the latest domain competencies and soft skills. The idea is to hire candidates with both knowledge (theoretical and practical) and experience in reflection and collaboration skills. Universities can not always keep up with the development of technology and the adaptation of curricula because change happens so quickly. Therefore, upgrades are needed in engineering education. Focusing education solely on measurable knowledge and results is a simple but often insufficient approach to presenting the entire curriculum within the time frame of a course. Our study of about 150 engineering students in Poland and Norway included the role of students' experiences in hands-on activities, especially labs. The research aimed to determine what students expect, what influences their results, and what complications may arise. The study results show that students often struggle with time management, prioritizing, and understanding practical tasks. In addition, there is a difference between students with and without practical work experience. Our research showed that both groups of students did not achieve all their intended educational goals for various reasons. Therefore, adapting the current teaching system to the student's needs and requirements is important. © 2023 IEEE.

关键词： Bolognia process culture model engineering education laboratory teaching soft skills

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Channel Hopping Steganography For Color Images 7

Channel Hopping Steganography For Color Images

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7th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2023

作者： Sanli, Yusuf Kilicaslan, Mahmut Demirci, Recep Graduate School of Natural and Applied Sciences Gazi University Computer Engineering Ankara Turkey Ankara University Computer Technologies Department Ankara Turkey Ankara Science University Computer Engineering Faculty of Engineering and Architecture Ankara Turkey

ISBN: (纸本)9798350342154

Internet has spread to quite large area and has a great share in communication systems. The protection of data during transfer from one point to another has also gained importance because of attacks. Data can be sent and received as sound, image, text etc. Structure of digital image offers us advantages for hiding while transferring data. Therefore, the use of steganography, known as confidentiality, by means of digital images will reduce violations from any external environment and consequently allow us to communicate reliably. Least significant bit (LSB) is well known method for image steganography where channel to be used is predefined. Thus, it is weak approach basically. In this study, channel hopping strategy where the channel to be used to store data bits are changed with a pseudo-random number generator (PRNG). A chaotic logistic map was used as PRNG, and its parameters were assigned as key vector. Consequently, confidentiality of data hiding has been increased. © 2023 IEEE.

关键词： Steganography

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